Reviewerly/exHarmony huggingface dataset:
Ten Myths About Blockchain Consensus

Blockchain and distributed ledger technologies have become key to develop secure decentralized applications. Their potential applications span various industries and their use-cases have already demonstrated the potential of such applications. Interestingly, these technologies rely on a large body of complex research topics like the Byzantine consensus problem. Although such problem was defined four decades ago, its subtle ramifications are largely misunderstood by many blockchain developers, let alone application programmers who build upon these blockchains. These misconceptions are dramatic as they prevent these applications from working efficiently and they make them vulnerable to attacks. This chapter debunks the major myths about blockchain consensus. First, it lists ten myths about blockchain consensus that are appearing frequently in the blockchain community. Second, it presents clarifications that debunk these myths to help build safer and more efficient blockchain and distributed ledger systems. These misconceptions are illustrated with the evaluation of three distributed ledgers, Hyperledger Fabric, Red Belly Blockchain, and R3 Corda, as well as three important consensus algorithms, BFT-SMaRt, Democratic BFT, and HotStuff.
The Future Ring Confidential Transaction Protocols for Privacy-Preserving Blockchain

In this chapter, we are going to give some potential future enhancement on the privacy-preserving blockchain protocol, the Ring Confidential Transaction (RingCT) protocol, which is currently used in the largest cryptocurrency Monero. Our future enhancements include the shortening of the size significantly, from linear to logarithm to the number of possible senders of the transaction. Another cutting-edge enhancement is to provide post-quantum security to the whole RingCT protocol, by using lattice-based cryptography to redesign all algorithms.
Blockchain for Health Data Management

This chapter presents a blockchain-based solution to build an e-health data management based on a hybrid edge cloud system. The proposed system enables the provision of real-time health service and provides secure user access to clouds, with privacy and security guarantees. Two smart contracts are designed to provide efficient health data storage and trustworthy data exchange among patients and healthcare providers. A case study is presented using an Ethereum blockchain platform with cloud computing to demonstrate the feasibility of the proposed e-health system in terms of data sharing efficiency, system security, and latency.
When Blockchain Meets Smart Cities: Opportunities, Security and Future Research

Smart cities use information and communication technologies to offer efficient management of services, resources and infrastructure to improve the quality of life for their citizens while addressing environmental and urban challenges and promoting economic growth. The smart city concept relies on digital connectivity and a growing number of networked smart devices and sensors deployed in cities and urban environments enabling real-time data collection and analytics for data-driven decision-making in various smart city applications. In this chapter, we explore how blockchain technology can address some of the application-specific challenges in smart cities and identify open issues and future research directions for the adoption of blockchain in smart city applications. The chapter further focuses on the potential of blockchain technology in enhancing the security of smart city applications.
Algorand Blockchain

Algorand is a blockchain-based cryptocurrency whose native token is called ALGO. The consensus protocol of Algorand is based on a Byzantine agreement protocol where each round of the protocol consists of several steps and each step is executed by a subset of users. Algorand is a proof-of-stake blockchain meaning that the number of ALGO tokens in each user's account determines the probability that the user is selected to take part in each step of the protocol. This chapter presents the main components of the Algorand protocol as well as the main use cases of the Algorand blockchain.
Cryptographic Primitives

Blockchain is a promising and growing technology for providing reliable and secure decentralized solutions. Nevertheless, it encounters many research challenges. Some of the main challenges in the blockchain are smart contract management, key management, and further improvements in security, privacy, and scalability. These challenges can be addressed by leveraging suitable and efficient cryptographic primitives. Therefore, this chapter aims to investigate and present a brief description of cryptographic primitives employed in the blockchain. Additionally, for each cryptographic primitive, a few research problems are also postulated that can be of independent interest.
Layer 2 Scaling Solutions for Blockchains

Many blockchain protocols suffer from scalability issues. One way to improve the scalability of existing solutions is to improve the underlying consensus algorithms. Another way is to carry out off-chain transactions in such a way that the interaction with the underlying blockchain is minimized. This class of solutions is known as layer 2 solutions. We discuss the motivation of layer 2 scaling solutions and various types of layer 2 scaling solutions. We discuss payment channel networks in details.
Supply Chain Management Using Blockchain

Among many domains being disrupted by blockchains, supply chains are at the top, as they naturally involve multi-party collaboration and regulatory restrictions spanning geographic boundaries, as well as they often demand greater transparency and trust. Blockchain technology can transform supply chains by enhancing trust, improving efficiency, and enabling regulatory compliance. This chapter discusses challenges related to the adoption of blockchains in supply chain settings and presents various solutions that address those challenges. In this regard, the chapter focuses on suitability analysis, multi-party integration, supply chain item tracking, digital-physical linkage, and business process management.
The Ethereum Blockchain: Implementation and Security Aspects

Firstly introduced in 2013, Ethereum revolutionized the concept of blockchain. Compared to the previous generation of blockchains with the main representative the Bitcoin, the Ethereum blockchain extends the applications of this technology to domains such as its transfer of information and inclusion of off-chain data inside the blockchain. Furthermore, with the introduction of smart contracts, Ethereum allows for the automatic execution of operations inside the blockchain. These represent the key steps Ethereum firstly posed to enhance the blockchain technology and lead it to its widespread application. In this chapter we present an overview of the fundamental aspects that characterize the Ethereum blockchain. We discuss both its implementation from a blockchain point of view and the fundamental algorithmic aspects that characterize its functioning. Furthermore, we discuss the known vulnerabilities and security issues related to all the presented building blocks of Ethereum. Our aim is to provide a clear state-of-the-art overview of both the implementation aspects and security of the Ethereum blockchain.
A dynamic approach for selecting load balancing algorithm in cloud

The collection of interconnected computers that constitutes more than one united computing resource is known as the Cloud. In recent years, the advancement of cloud computing has facilitated the rapid arrangement of interconnected data centers that are geographically dispersed, offering high-quality and dependable services. Scalable traffic management has recently been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, reducing latency during multidimensional resource allocation still remains a challenge. Hence, there is a need for efficient resource scheduling to ensure load optimization in the cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The goal of the proposed system is to select the required load balancing algorithm to enhance resource utilization. Simulations were conducted to evaluate effectiveness using the Cloudsim simulator in cloud data centers, and the results show that the proposed method achieves better performance in terms of the average success rate, resource scheduling efficiency, and response time. The dynamic nature of cloud environments requires constant adaptation in resource allocation strategies. This necessitates the development of algorithms capable of handling diverse workloads efficiently. Additionally, the increasing complexity of applications and services hosted on the cloud demands a comprehensive approach that considers not only load balancing but also the intricacies of resource utilization. Furthermore, the proposed algorithm focuses on predictive analytics to anticipate fluctuations in demand and adjust resource allocation preemptively. By incorporating machine learning techniques, the system can adapt to changing patterns, ensuring optimal performance even in unpredictable scenarios. This holistic approach addresses the evolving challenges in cloud computing, providing a robust foundation for reliable and efficient service provisioning.
Effects of a Brief Mindfulness Intervention on Perceived Stress, Affect, and Trait Mindfulness in College Students

The current study was interested in the effects of a quick mindfulness meditation intervention on college students perceived stress, affect, and trait mindfulness. Students were placed into the following three groups: Silent Meditation (n = 16), Guided Meditation (n = 12), Control (No Meditation; n = 72). Each experimental group took part in a four-week mindfulness meditation intervention comprised of one session per week. Students perceived stress, affect, and trait mindfulness were measured pre-intervention, during the intervention, and post-intervention. Perceived stress was measured using the Student Stress Inventory and the College Stress Scale, affect was measured using the Positive and Negative Affect Schedule, and mindfulness was measured using the Kentucky Inventory of Mindfulness Skills. As predicted, students in the silent meditation group reported a statistically significant decrease in perceived stress over the four weeks and an increase in trait mindfulness. Although not statistically significant, the guided meditation also reported a decrease in perceived stress and increase in mindfulness. Results suggest that a short mindfulness intervention may be useful in helping college students deal with stressors of college life.
Duadic codes over Fp + uFp + vFp + uvFp

Duadic codes constitute a well-known class of cyclic codes. In this paper, we study the structure of duadic codes of length n over the ring R = Fp + uFp + vFp + uvFp, u2 = v2 = 0, uv = vu, where p is prime and (n, p) = 1. These codes have been studied here in the setting of abelian codes over R, and we have used Fourier transform and idempotents to study them. We have characterized abelian codes over R by studying their torsion and residue codes. It is shown that the Gray image of an abelian code of length n over R is a binary abelian code of length 4n. Conditions for self-duality and self-orthogonality of duadic codes over R are derived. Some conditions on the existence of self-dual augmented and extended codes over R are presented. We have also studied Type II self-dual augmented and extended codes over R. Some results related to theminimum Lee distances of duadic codes over R are presented. We have also presented a sufficient condition for abelian codes of the same length over R to have the same minimum Hamming distance. Some optimal binary linear codes of length 36 and ternary linear codes of length 16 have been obtained as Gray images of duadic codes of length 9 and 4, respectively, over R using the computational algebra system Magma.
Enhancing Handwritten Alphabet Prediction with Real-time IoT Sensor Integration in Machine Learning for Image

Abstract Handwriting Recognition (HWR) is a difficult and varied discipline having applications in a wide range of fields, including banking, education, and administration. This research investigates the two main types of HWR systems: online and offline character recognition. Online HWR entails real-time input utilizing digital pens to capture dynamic handwriting traits. It’s used in contemporary gadgets like tablet computers and for signature verification. Offline HWR, on the other hand, processes scanned documents, making it important in situations such as bank cheque processing and assisting the visually handicapped. The research emphasizes the continuing potential for progress in HWR, notably using machine learning and deep learning approaches. Machine learning, a subset of Artificial Intelligence (AI), is critical in developing character recognition algorithms. The selection of an effective classification model is a vital decision, and the study uses a specific dataset to conduct a comparison analysis of alternative models to help in this process. Such assessments provide useful insights for academics and practitioners, allowing them to make more informed judgements on model development for HWR applications.
Automatic document classification: the role of interclass similarity

The continuous increase of information in digital format requires new methods and techniques to access, collect and organize these volumes of textual information. One of the most widely used techniques to organize information is the automatic classification of documents. Automatic text classification systems have a low efficiency when the classes are very similar, i.e. there is overlap between them, and in this case it is very important to be able to identify those attributes that allow us to separate one class from another. In this paper we present the relationship between overlap between classes and classification accuracy. A public corpus with four classes is used for the evaluation, and each class is further separated by positives and negatives. The results obtained from four subsets with different number of training instances are presented, for each case the similarity plots, the accuracy value and the confusion matrices obtained are presented. The results obtained are very illustrative and show that the higher the similarity between classes, the lower the classification accuracy.
Bibliometric analysis for the determination of fields of opportunity for new technological trends: augmented reality as quality control

With the emergence of industry 4.0, innovation was made in terms of emerging technologies, one of them is Augmented Reality as a tool that manages to enhance the new generation of automated factories. This leads to a use by operators or any type of personnel who need interaction with specific information such as: product data sheets, instructions for maintenance procedures, assembly, manufacturing, quality control forms etc. just to mention a few of the applications. They can be handled efficiently and amplifying the degree of ease of these processes by applying AR. Therefore this is the reason why this research is carried out taking into account different environments, the objective is to provide an overview in which fields of opportunity or trends are identified on the use of this pillar of i4.0 within the quality control process, with the intention of showing approaches that motivate scientific research, to achieve this goal, a bibliometric analysis was applied to a database obtained in Scopus, using the R software, with which authors, research topics, journals of interest, keywords, scientific production by year or country were identified. With the intention of finding the most significant information, so the results obtained show 5 potential areas where AR can play an important role within the quality control process demonstrating its suitability to improve processes.
Digitalization of motivational features as a way to organize independent work of bachelors

Problem statement . Effective organization of extracurricular work for future bachelors involves the construction of individual learning trajectories, based, among other things, on the level of motivation to study specialized disciplines. The expediency of using intelligent information technologies in classifying the composition and forms of independent work of bachelors of applied mathematics is substantiated. Methodology. Applying the developed mobile application, multi-parameter motivational characteristics of students were identified. The resulting motivational profiles are clustered into five motivational groups, similar in sign values. The construction and analysis of multi-parameter classification were carried out via cluster analysis and neural network technologies. Clustering of motivational groups and the application of appropriate strategies for organizing independent work were realized at the Faculty of Computer Technologies and Applied Mathematics of Kuban State University. Results. The constructed neural network classifies the bachelor's motivational profile, assigns him a strategy for independent work, defining the specific values of the variable elements of the strategy. Strategies for organizing independent work have been established for clustered motivational groups. Conclusion. Construction and clustering of motivational profiles allows you to adjust individual strategies for independent learning of bachelors of applied mathematics, determine the values of variable elements and, as a result, not only develop professional skills, but also develop skills in organizing your own work process, resource allocation, and identifying work stages.
The current state and prospects of modeling digital professional spaces in business and education

Problem statement . As world becomes more and more digital, there is an increasing need for modeling digital professional spaces (DPS) that help manage business processes more quickly. DPSs are computer systems designed to improve the efficiency of such types of work activities as cooperation, communication and data exchange. Modeling of the DPSs at the university will speed up the subsequent professional adaptation of graduates. The purpose of the study is to describe the methodological, theoretical and technological foundations of the DPSs’ modeling. Methodology . System-structural, system-activity, and competency-based approaches were used. Content analysis and thematic monitoring of digital content modeling were carried out. Results . A model for the design of DPS is presented, which includes several key components, such as user needs assessment, cognitive management, DPS modeling, designing the organizational structure of business processes, data modeling and development of scenarios for the use of information systems, educational engineering. The importance of each component is analyzed, examples of how they can be implemented in practice, several problems were discussed and potential solutions were proposed. An overview of the main concepts and approaches related to the modeling of DPS, including their definition, characteristics and main problems, is provided. The main methods and tools used to model these environments are considered. The most promising areas of research in this area are identified. Conclusion . Modeling and using of DPS in higher education has the potential to improve the quality and accessibility of education, while preparing students for a rapidly changing labor market. DPSs have the potential to digitally transform higher education by providing students with an immersive, personalized and engaging learning experience that can prepare them for future careers and improve their overall learning outcomes.
An Image Compression and Encryption Approach with Convolutional Layers, Two-Dimensional Sparse Recovery, and Chaotic Dynamics

This research presents a method to encrypt and compress images using two-dimensional sparse decomposition, chaotic systems, and convolutional layers. The original image is first encrypted via the convolutional layers in the proposed approach, inspired by deep neural networks. This step is called CLE, which stands for convolution layer-based encryption, as it helps to increase the security level of encryption. Then, the encrypted image is compressed by two orthogonal matrices created by the chaotic systems and singular value decomposition. After that, the compressed image is quantized and mapped. In the next step, the pixels of the quantized matrix are scrambled to reduce the correlation between neighboring pixels. Then, the XOR operation is applied for the final part of the encryption process. In the decryption process, the direct inverse operators cannot be used. Therefore, the convolutional layer-based sparse decomposition (CLSD) approach is proposed to recover the original image by half quadratic splitting approach. Due to HQS, the proposed decryption approach is converted to two repetitive steps. The simulation results and the security analyses demonstrate that the proposed image reconstruction method performs well for various compression ratios.
Classification of Chaotic Dynamics Through Time–Frequency Representations and Machine Learning

This study employs an approach that centers on the utilization of support vector machine classifiers to categorize chaotic dynamics. Commonly, within chaotic secure communication schemes, one state variable of a determined dynamical system is employed for encrypting information, while another operates as the cryptographic key. Successful communication requires the receiver to synchronize using this key state. In the event of a security breach or unauthorized access to this data, the ability to accurately identify the specific chaotic system and its state becomes essential for preventing potential attacks. This chapter presents an approach to the classification of distinct chaotic dynamics based on time-domain data observation. The proposed method combines Time–Frequency Representations (TFR) with machine learning, utilizing Support Vector Machine (SVM) classifiers, resulting in effective classification of both the chaotic system and its state. This approach extends to multiclass classification, where we employ an error-correcting output codes approach to decompose complex tasks into simpler, binary problems, thereby enhancing both scalability and versatility, as indicated by the results demonstrating good performance.
Optimization of Echo State Neural Networks to Solve Classification Problems

Echo State Neural Networks (ESNN) are a kind of recurrent neural networks, which are computationally very cheap to train: input and reservoir weights are initialized randomly, the output weights are calculated by linear regression. The cost of linear regression is equivalent to invert a matrix. In this work, the five parameters of an ESNN, input noise, leaking rate, reservoir size, the seed of the random generator for the reservoir weights initialization, and the seed for a permutation of the reservoir weights, are optimized by the heuristics genetic algorithm and pattern search to solve classification tasks. We show that with these five parameters one instance of an ESNN is completely characterized. With these five parameters it is not possible to perform an exhaustive search of their values. We show with several public datasets the results of the optimization and the comparison with the manual optimization of only the reservoir size.