Reviewerly/exHarmony huggingface dataset:
Disrupting Central Banking or Shadow Central Banking

Abstract Private entities have recently attempted to invade the realm of central banking. This chapter identifies such activity as ‘shadow central banking’, and draws analogies to the previous emergence of ‘shadow banking’ more generally. The similarities between shadow banking and shadow central banking are clear when analysing the circumstances of their emergence, their pathologies, and the regulatory response. This chapter focuses on the evolving roles of central banks and private initiatives, as their activities have led to the rethinking of central banking activities and the form of money in modern capitalism. In doing this, it proposes that this disruption of traditional central banking activity stems from a new interpretation of the concept of money, one that defies the classic dichotomy of ‘central bank money’ versus ‘commercial bank money’. This chapter then analyses the main characteristics of shadow central banking and identifies specific commonalities not only with the shadow banking system, but also with historical systems that pre-existed the modern central banking framework. Finally, it considers the systemic risks, systemic conflicts of interest and opaque corporate governance mechanisms associated with shadow central banking and posits some policy considerations to address such risks.
Starting from the Basics

Abstract This chapter explores the problem of systematization entailed in smart contracts. From a commercial law perspective, it discusses whether the rules provided in the commercial context for the conclusion of contracts between two ‘human’ parties may be safely extended to blockchain smart contracts. After analysing smart contracts and their main properties, this chapter surveys the relevant North American and European regulations. The chapter suggests that applying the existing regulatory framework to the new emerging commercial practices is the best solution and emphasizes the need for uniform principles as a precondition for commercial practices to prosper. It takes into consideration important historical precedents, such as the Law of Merchants (lex mercatoria), and its new form, the so-called lex cryptographia (or cryptographica), as the correct paradigms to be applied to the globalized cryptographic-based context. Finally, this chapter emphasizes the need for both public regulation and self-regulation as two complementary sources in commercial law, and the role that self-regulatory organizations and industry representatives should play in the shift towards a new technologic area.
Enhancing Disruption

Abstract This chapter provides an analysis of initial coin offerings, their development in the market, and their role in shaping the development of the cryptoeconomy. It identifies the initial ICO ‘structural’ pattern and its subsequent evolutions. It then characterizes ICOs in three major jurisdictions—the United States, Europe, and Switzerland. Furthermore, this chapter considers specific unsolved issues as they emerged in the markets and legal systems. In doing so, it focuses on the lack of a definitive approach for regulating ICOs, as well as on specific promises that ICOs breached, in particular with respect to financial inclusion, disintermediation, and infrastructural transformations. Finally, this chapter identifies the main consequences brought by ICOs, with an emphasis on capital market structures and digital securities, which triggered multiple transformations at multiple levels, including corporate governance, development of digital assets, and non-fungible tokens.
Video deepfake detection using Particle Swarm Optimization improved deep neural networks

Abstract As complexity and capabilities of Artificial Intelligence technologies increase, so does its potential for misuse. Deepfake videos are an example. They are created with generative models which produce media that replicates the voices and faces of real people. Deepfake videos may be entertaining, but they may also put privacy and security at risk. A criminal may forge a video of a politician or another notable person in order to affect public opinions or deceive others. Approaches for detecting and protecting against these types of forgery must evolve as well as the methods of generation to ensure that proper information is supplied and to mitigate the risks associated with the fast evolution of deepfakes. This research exploits the effectiveness of deepfake detection algorithms with the application of a Particle Swarm Optimization (PSO) variant for hyperparameter selection. Since Convolutional Neural Networks excel in recognizing objects and patterns in visual data while Recurrent Neural Networks are proficient at handling sequential data, in this research, we propose a hybrid EfficientNet-Gated Recurrent Unit (GRU) network as well as EfficientNet-B0-based transfer learning for video forgery classification. A new PSO algorithm is proposed for hyperparameter search, which incorporates composite leaders and reinforcement learning-based search strategy allocation to mitigate premature convergence. To assess whether an image or a video is manipulated, both models are trained on datasets containing deepfake and genuine photographs and videos. The empirical results indicate that the proposed PSO-based EfficientNet-GRU and EfficientNet-B0 networks outperform the counterparts with manual and optimal learning configurations yielded by other search methods for several deepfake datasets.
Determination of the bilingualism coefficient among future subject teachers by the method of associative experiment

The paper describes the essence of the phenomenon of bilingualism, types of bilingualism, approaches to its quantitative assessment, as well as the results of an associative experiment aimed at determining the level of bilingualism of future teachers studying at Kazan Federal University on a bilingual basis using Russian and Tatar languages. The experiment involved 40 students – future mathematics teachers in the 2nd and 3rd years of study. The scientific novelty of the research lies in determining that the level of the academic component of bilingual communicative competence among future bilingual teachers is higher than the level of their general communicative competence. As a result, it was found that at this stage of the implementation of the multilingual education model at Kazan Federal University, most future subject teachers have formed the coordinate and the compound type of Tatar-Russian bilingualism both in the field of general communication and academic language in the sphere of mathematics on the topic “Function”.
Digital Therapeutic Cultures and Their New Regime of Psychological Truth

The article focuses on contemporary digital therapeutic cultures’ new regime of truth. This entails describing and critically analysing the sociomaterial apparatuses that distinguish truth from false and produce specific modes of subjectivation. The article shows that the digital regime of psychological truth is heir to the behavioural mistrust of subjectivity and the epistemological shift from the causal-comprehensive model towards the probabilistic-predictive one. However, the psychological subject has not been excluded, but her role has changed. The article introduces the distinction between valuable and spurious truths to analyse this shift. Algorithms do not need the psychological subject to produce true outcomes, but they depend on her and psy-knowledges to distinguish significative from irrelevant truths. Following Maurizio Lazzarato, the article concludes that digital therapeutic cultures share one of the main features of contemporary capitalism: to produce value at the intersection of processes of subjectivation and de-subjectivation.
The significance of an authentic author’s fairy tale in developing the emotive competence of middle school students in foreign language classes in the additional education system

The purpose of the study is to identify didactic possibilities and provide theoretical justification for the feasibility of using an authentic literary fairy tale in the formation of emotive competence when teaching schoolchildren a foreign language in the system of additional education. The article analyzes the various didactic possibilities of literary fairy tales when teaching a foreign language to middle-level students. The scientific novelty lies in the development and testing of a set of exercises based on the literary fairy tale “A Bear Called Paddington” by Michael Bond, involving students in the process of foreign language communication and ensuring the formation of elements of emotive competence. As a result of working with fiction, children develop not only language skills, but also emotional intelligence. Students learn how to determine the emotional nature of a text and later use emotionally charged vocabulary in monologue speech, which contributes to the formation of emotive competence.
Research on dual robot collaboration method based on improved double ant colony algorithm

Purpose This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations. Design/methodology/approach A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy. Findings The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems. Originality/value This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.
Enhancing Central Model Performance: Leveraging Federated Learning Across Virtual Machine Networks for Distributed Training and Synchronization

This project takes a closer look at federated learning as a way of achieving superior machine learning models in a distributed manner while preserving privacy in the datasets that contribute. We have modelled a network of cooperating virtual machines working collectively without explicit sharing of data. Rather than distributing the complete big dataset to each system, we have split it into chunks of 10,000, 5,000, 40,000, 5,000 entries. These systems would then work on their data with learning rates of their model's making and in the decision-making processes to modify their settings, so that the data that systems would work on could allow for building their respective models by them. What this means is that the high point in the project is the combination of these models into one overarching model. The overarching model then gets better because of the small models learning from it without having to access the data associated with the models in a direct sense. This way, a better model can be built, which will intimately understand the data and thereby predict more accurately. Taken as a whole, we have shown how federated learning can improve the models of machine learning in a significantly private manner, and thus the methodology is positively postured with respect to future related work
Modernizing and Revolutionizing Land Record Management with Blockchain

In the modern era, access to land records has become essential, enabling individuals to verify land ownership for both personal and business purposes. In India, managing land records has proven to be a formidable challenge given the country's vast population. The current system has suffered from incomplete registries, which, in turn, has facilitated fraudulent activities. Moreover, the existing government system is plagued by inconsistencies and involves numerous intermediaries with access to the records, raising concerns about unauthorized alterations. With the increasing acceptance of blockchain technology, the government is actively exploring its implementation to address these issues. In a world moving towards Web 3.0, integrating land records into a blockchain offers significant advantages, primarily through enhanced transparency. This approach ensures that citizens have unhindered access to land records, complete with visual representations on maps, promoting greater inclusivity and accountability
A Blockchain-based federated learning framework for secure aggregation and fair incentives

Federated Learning (FL) has gained prominence as a machine learning framework incorporating privacy-preserving mechanisms. However, challenges such as poisoning attacks and free rider attacks underscore the need for advanced security measures. Therefore, this paper proposes a novel framework that integrates federated learning with blockchain technology to facilitate secure model aggregation and fair incentives in untrustworthy environments. The framework designs a reputation evaluation method using quality as an indicator, and a consensus method based on reputation feedback. The trustworthiness of nodes is dynamically assessed to achieve an efficient and trustworthy model aggregation process while avoiding reputation monopolisation. Furthermore, the paper defines a tailored contribution calculation process for nodes in different roles in an untrusted environment. A reward and punishment scheme based on the joint constraints of contribution and reputation is proposed to attract highly qualified workers to actively participate in federated learning tasks. Theoretical analysis and simulation experiments demonstrate the framework's ability to maintain efficient and secure aggregation under a certain degree of attack while achieving fair incentives for each role node with significantly reduced consensus consumption.
The technology of creative collective project “Illustrated Book” as a means of developing professional competencies of students – future preschool and elementary education teachers

The research aim is to justify the use of the technology of organizing a creative collective project “Illustrated Book” as a means of enlightenment, popularizing cultural heritage, and enhancing students’ cognitive, communicative, managerial, methodological, creative, and reflexive components of a teacher’s professional competence. The article addresses the issue of developing professional competencies of pedagogical university students (future preschool educators and elementary school teachers). The novelty of the study is determined by presenting an author’s step-by-step technology for familiarizing students with works of art and organizing creative collective projects, giving students the opportunity to develop organizational, pedagogical, and artistic skills necessary for their future professional activities. As a result of the research, the structure, stages, conditions, organization options, and implementation of collective student projects “Illustrated Book” as a means of developing professional competencies of pedagogical university students have been developed and substantiated.
Transformation of Elliptic Curve Discrete Logarithm Problem to QUBO Using Direct Method in Quantum Annealing Applications

This paper investigates how to reduce the elliptic curve discrete logarithm problem over prime fields to the quadratic unconstrained binary optimization (QUBO) problem in order to obtain as few logical qubits as possible. In the best case scenario, if n is the bitlength of a characteristic of prime field Fp, approximately 3n³ logical qubits are required for such a reduction in the Edwards curve case. We present a practical attack on an elliptic curve discrete logarithm problem over the 3-bit prime field F7 for an elliptic curve with the subgroup of order 8. We solved this problem using the D-Wave Advantage QPU. To the best of the authors' knowledge, no one has made, so far, a practical attack on the elliptic curve discrete logarithm over a prime field using the direct quantum method.
Flipover outperforms dropout in deep learning

Abstract Flipover, an enhanced dropout technique, is introduced to improve the robustness of artificial neural networks. In contrast to dropout, which involves randomly removing certain neurons and their connections, flipover randomly selects neurons and reverts their outputs using a negative multiplier during training. This approach offers stronger regularization than conventional dropout, refining model performance by (1) mitigating overfitting, matching or even exceeding the efficacy of dropout; (2) amplifying robustness to noise; and (3) enhancing resilience against adversarial attacks. Extensive experiments across various neural networks affirm the effectiveness of flipover in deep learning.
Self-attention Guidance Based Crowd Localization and Counting

Most existing studies on crowd analysis are limited to the level of counting, which cannot provide the exact location of individuals. This paper proposes a self-attention guidance based crowd localization and counting network (SA-CLCN), which can simultaneously locate and count crowds. We take the form of object detection, using the original point annotations of crowd datasets as supervision to train the network. Ultimately, the center point coordinate of each head as well as the number of crowds are predicted. Specifically, to cope with the spatial and positional variations of the crowd, the proposed method introduces transformer to construct a globallocal feature extractor (GLFE) together with the convolutional structure. It establishes the near-to-far dependency between elements so that the global context and local detail features of the crowd image can be extracted simultaneously. Then, this paper designs a pyramid feature fusion module (PFFM) to fuse the global and local information from high level to low level to obtain a multiscale feature representation. In downstream tasks, this paper predicts candidate point offsets and confidence scores by a simple regression header and classification header. In addition, the Hungarian algorithm is used to match the predicted point set and the labelled point set to facilitate the calculation of losses. The proposed network avoids the errors or higher costs associated with using traditional density maps or bounding box annotations. Importantly, we have conducted extensive experiments on several crowd datasets, and the proposed method has produced competitive results in both counting and localization.
Impact of temperature and solar irradiance in shadow covering scenarios via two-way sensitivity analysis for rooftop solar photovoltaics

Solar irradiance and temperature are two primary factors that affect the energy generation efficiency of solar photovoltaic (PV) systems, meaning that climate change may significantly impact the production of solar energy in the future.In this study, a two-way sensitivity analysis is carried out to determine the energy generation potential under future climate change conditions, and conditions of shadow covering are also considered.The simulation results indicate that the impact of solar irradiance causes the most variation in energy generation, and that shadow covering has a weak impact on the output.In the one-way sensitivity analysis, solar irradiance makes a ± 18% difference in energy generation, whereas shadow covering represents about ± 2%.In the future trend of climate change in the world and Taiwan, the energy generation potential can decrease up to 21% and increase 8%, respectively, mainly corresponded by solar irradiance.We suggest that compared with the issue of temperature, the efficiency of solar irradiance usage is more important, especially in view of the global trend toward solar irradiance loss.Taiwan also suffers the issue, but the different trend of climate change impact may make the challenge different.
Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies

Since requirement dependency extraction is a cognitively challenging and error-prone task, this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies. In this paper, the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles: uncertainty probability, text similarity difference degree and active learning variant prediction divergence degree. By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs, the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples. As the optimal samples are continuously added into the initial training set, the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved. Therefore, compared with other active learning strategies, a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples. Finally, the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%, the weight-recall by 2.45%, and the weight-precision by 2.64% in comparison with other strategies, saving approximately 46% of the labelled data compared with the machine learning approach.
The AI empathy effect: a mechanism of emotional contagion

While advances in technology have led to the widespread use of artificial intelligence (AI) in frontline services, the AI empathic effect is often overlooked. Based on emotional contagion theory, this research explores the impacts of AI emotional mimicry and empathic concern in service success and failure via experimental designs, respectively. Results indicate that AI with high emotional mimicry and empathic concern resulted in higher arousal and pleasure, increasing tourists’ continuous usage intention. Arousal and pleasure played significant mediating roles in service success. However, while empathic concern significantly affected continuous usage intention through pleasure, arousal did not have a similar effect. The joint effects of extrinsic and intrinsic anthropomorphism (emotional mimicry) were significant in a successful service context, but not in service failure. These results have important implications for the development and promotion of AI and enriches theoretical applications of emotional contagion theory and empathy.