Reviewerly/exHarmony huggingface dataset:
Digital RMB vs. Dollar Hegemony? Friendly Foes in China-US Currency Competition

Abstract Digital transformations are impacting inter-state currency politics in strategically important ways. This article contributes to growing debates over the nature of US-China competition as the Chinese RMB rapidly digitizes in ways said to challenge the hegemony of a slower digitizing greenback. We categorize existing views of currency competition in the digital age into two categories: ‘conventional transformation’ and ‘transformative continuity’. Both these presently dominant perspectives, we argue, are overly techno-deterministic and stand in contrast to a third perspective we propose called ‘probabilistic flux’. Emphasizing the unanticipated and error-prone nature of technological change through Social Construction of Technology theory and informed by the IPE of monetary relations, we provide a more nuanced assessment of digital RMB’s challenges to dollar dominance stressing the functions, benefits and powers of international currency hegemony. Our conclusions are three-fold. First, wider digital currency alternatives to both the dollar and RMB have enriched the international currency functions of the former over the latter. Second, this broader array of digital currency alternatives combines with Chinese RMB digitization to gradually erode the functional base and benefits of dollar dominance position, as well as diminish the US’s international monetary power in both Asia and beyond. Third, what we see as largely friendly digital currency competition focused on domestic imperatives currently remains unpredictable. These findings pose present possibilities for greater international cooperation but equally for less friendly competition and flux particularly as US dollar digitization also unfolds in ways that are difficult to anticipate.
Information Access Effort: The Role of Head Movements for Information Presented at Increasing Eccentricity on Flat Panel and Head-Mounted Displays

This experiment examined performance costs when processing two sources of information positioned at increasing distances using a flat panel display and an augmented reality head-mounted display (AR-HMD).The AR-HMD enables positioning virtual information at various distances in space. However, the proximity compatibility principle suggests that closer separation when two sources of information require mental integration assists performance, whereas increased separation between two sources hurts integration performance more than when a single source requires focused attention. Previous studies have provided inconsistent findings regarding costs associated with increased separation. Few of these experiments have examined separation for both focused and integration tasks, compared vertical and lateral separation, or measured head movements.Three experiments collectively examined these issues using a flat panel display and a virtual display presented with an HMD, where the separation of information varied laterally or vertically during a focused attention (digit reading) task and an information integration (mental subtraction) task.There was no performance cost for either display when information was increasingly separated. However, head movements mitigated performance costs by preserving accuracy at larger separations without increasing response time.Head movements appear to mitigate performance costs associated with presenting information increasingly far apart on flat panel displays and HMDs. Both eye scanning and head movements appear to be less effortful than expected.These findings have important implications for design guidelines regarding the placement of information presented on flat panel displays and, more specifically, HMDs, which can present information 360° around the user.
Interoperability-Enhanced Knowledge Management in Law Enforcement: An Integrated Data-Driven Forensic Ontological Approach to Crime Scene Analysis

Nowadays, more and more sciences are involved in strengthening the work of law enforcement authorities. Scientific documentation is evidence highly respected by the courts in administering justice. As the involvement of science in solving crimes increases, so does human subjectivism, which often leads to wrong conclusions and, consequently, to bad judgments. From the above arises the need to create a single information system that will be fed with scientific evidence such as fingerprints, genetic material, digital data, forensic photographs, information from the forensic report, etc., and also investigative data such as information from witnesses’ statements, the apology of the accused, etc., from various crime scenes that will be able, through formal reasoning procedure, to conclude possible perpetrators. The present study examines a proposal for developing an information system that can be a basis for creating a forensic ontology—a semantic representation of the crime scene—through descriptive logic in the owl semantic language. The Interoperability-Enhanced information system to be developed could assist law enforcement authorities in solving crimes. At the same time, it would promote closer cooperation between academia, civil society, and state institutions by fostering a culture of engagement for the common good.
Error Suppression for Arbitrary-Size Black Box Quantum Operations

Efficient suppression of errors without full error correction is crucial for applications with noisy intermediate-scale quantum devices. Error mitigation allows us to suppress errors in extracting expectation values without the need for any error correction code, but its applications are limited to estimating expectation values, and cannot provide us with high-fidelity quantum operations acting on arbitrary quantum states. To address this challenge, we propose to use error filtration (EF) for gate-based quantum computation, as a practical error suppression scheme without resorting to full quantum error correction. The result is a general-purpose error suppression protocol where the resources required to suppress errors scale independently of the size of the quantum operation, and does not require any logical encoding of the operation. The protocol provides error suppression whenever an error hierarchy is respected-that is, when the ancillary controlled-swap operations are less noisy than the operation to be corrected. We further analyze the application of EF to quantum random access memory, where EF offers hardware-efficient error suppression.
Green blockchain – A move towards sustainability

In recent years, blockchain technology has seen significant growth and widespread adoption in various industries. However, one major drawback of blockchain investments is their substantial energy consumption, which has negative impacts on both the economy and the environment. The main cause of concern is the generation of atmospheric carbon emissions resulting from excessive energy usage. This research study aims to identify blockchain networks and systems that assert themselves as environmentally friendly and determine which of them produces the least amount of carbon emissions, such as Cardano, Tezos, and Bitgreen. This has been accomplished by following a comprehensive hybrid literature review. Our study has identified 23 blockchain networks that consume significantly less power and release fewer carbon dioxide emissions compared to the Bitcoin network. Some of these environmentally friendly networks include Algorand, Fantom, MobileCoin, and Electroneum. Additionally, we have found various projects and organizations that support greener blockchain initiatives, such as the Renewable Energy Certificate Mechanism, Green Digital Finance Alliance, GreenTrust, and the Energy Web Foundation. While several projects in this area have been recognized and examined, comprehensive research and analysis are still needed to provide empirical evidence regarding the power consumption and carbon dioxide emissions of these claimed environmentally friendly blockchains. This is due to the relatively early stage of development in this field.
Vulnerability Exploitation Risk Assessment Based on Offensive Security Approach

Security incidents targeting control systems and the industrial internet of things (IIoT) are on the rise as attackers gain a better understanding of the nature of these systems and their increasing connectivity to information technology (IT). Every year, the number of vulnerabilities associated with these incidents increases, making it impractical to apply timely patches for all of them. The current vulnerability assessments, which are the basis for vulnerability patching, have limitations in that they do not adequately reflect the risk of exploitation in the real world after discovery and do not consider operational technology (OT) and industrial control system (ICS) environments other than IT environments. This study proposes to evaluate exploit risk in real-world environments by considering OT/ICS environments and calculating three metrics, including exploit chain risk, exploit code availability, and exploit use probability based on cyber threat information, including IIoT vulnerability data, used in OT/ICS environments. In addition, we construct exploitation scenarios in a control system environment to prioritize vulnerabilities with a high risk of exploitation based on the three metrics. We show that by assessing the risk of attackers’ intentions and exploited technologies for attacks against IIoT devices in a control system environment, we can provide defenders with comprehensive attack risk information for proactive defense.
Learning lessons from Murzuq-Libya meteorological station

In this study, an examination was conducted on weather data gathered from the Murzuq weather station over a period of nine months, specifically focusing on 15-minute time series solar radiation data. The data was sourced from the Center for Solar Energy Research and Studies in Tajoura-Tripoli, through a collaborative agreement between the Faculty of Engineering at Wadi Alshatti University and the research center. The information collected encompassed various solar radiation components, such as global horizontal solar radiation, direct normal radiation, sky-diffuse solar radiation, and ground reflected solar radiation. The aim of this study is to verify calculated values of these components using mathematical models by comparing them with their measured values. The investigation revealed that the Earth's reflectance value for the region was estimated and determined to be around 0.4. It is important to note that this figure was different from the typically advised value of 0.2 that was given in previous literature.
TWO-FACTOR USER AUTHENTICATION SYSTEM USING FACIAL RECOGNITION

In today's world of information technology, data security is becoming a paramount concern. One of the most effective protection methodologies is two-factor authentication. This article delves into a cutting-edge method of two-factor authentication based on the combination of neural networks and facial recognition. Deep learning, employed in neural networks, allows the system to adapt to minor changes in a user's appearance, such as a new hairstyle, the presence or absence of makeup, wearing glasses, and so on. This makes the system flexible and capable of recognizing the user even with slight alterations in their look. The core idea of the method is to analyze the unique features of the user's face. The neural network "learns" the characteristics of each user, creating their unique "portrait". This "portrait" is then used for identity verification upon attempting to access the system. In addition to facial recognition, the system may require password input or another form of authentication, making the login process even more secure. The combination of these two methods ensures a high level of protection against unauthorized access. A significant advantage of such a system is its convenience for the user. The user's face becomes the "key" to the system, making the login process quick and seamless. It's also worth noting that the advancement of facial recognition technology opens new horizons for data security. Using neural networks in conjunction with two-factor authentication may become the standard in the near future.
Blockchain, Decentralized Consensus, and Trust

This chapter sets the stage for comparing the problems of trust raised by standard and new money instruments. The computer science of blockchain is summarized in a brief discussion of hashing, time-stamping digital documents and consensus. Our simplified approach to these highly sophisticated issues is meant to fit our economic perspective. The principle of “no questions asked” is taken as the foundation of monetary economics. The problem of safe assets and collateralization is acknowledged as the monetary problem of the millennium. The hierarchy of account-based money is discussed. Bank runs and financial stability as depicted by Diamond and Dybvig are given due emphasis. Finally, we compare the different problems of trust that blockchain solutions and monetary institutions are meant to face. An Appendix argues that classical debates in monetary thinking can have a firm grip on the current evolution of digital money.
YOLO and Faster R-CNN object detection for smart Industry 4.0 and Industry 5.0: applications, challenges, and opportunities

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UAV-taken Wind Turbine Image Dehazing with a Double-patch Lightweight Neural Network

Unmanned Aerial Vehicles (UAVs) offer a solution for remote inspection of wind turbines. However, in stormy weather conditions, the visual quality of UAV-taken images is affected by contaminated suspended atmospheric particles. To address this problem, a double-patch lightweight convolutional dehazing neural network (DPLDN) is proposed to reconstruct hazy images and enhance the image quality. Unlike other learning-based methods that measure transmission map and atmospheric light separately, the proposed DPLDN uses a transformed atmospheric scattering model to jointly transmission map and atmospheric light, employs depth-separable convolution instead of conventional convolution, and splits the image into double patches. In addition, a super-resolution reconstruction method is proposed to transform the processed low-resolution images into higher-quality images. Extensive experiments shows that our proposed method has better dehazing performance compared to other state-of-the-art image dehazing techniques. Meanwhile, the applicability of the method in wind turbine blade image segmentation is experimentally verified.
Analysis of Time Series Data Generated From the Internet of Things Using Deep Learning Models

The ever-increasing widespread use of the Internet of Things and its applications has generated massive amounts of data. IoT sensor-generated datasets typically have a time-series structure and relational metadata to describe them. Time series data are typically data that have timestamps and can be obtained from sensors and IoT devices. Data prediction is required to maximize the potential of IoT-generated data, and anomaly detection and correction are needed to preserve data quality and integrity. Traditional machine learning models are incapable of analyzing the gigantic amounts of IoT-generated data. On the other hand, deep learning can properly analyze large data volumes, leading to increased use in the IoT domain. This research has examined the use of deep learning models for prediction, anomaly detection, and correction of data generated by IoT devices. The study found that deep learning is widely used in different fields today to analyze IoT-generated data. The research also outlines some challenges being faced while using deep learning models for IoT data analysis. More research is suggested in this study to expose more challenges and tackle the current challenges to achieve better IoT data analysis using deep learning.
A blockchain based solution to improve loyalty program with NFT in agribusiness

This paper presents the design and preliminary development a new model based on blockchain for improving brand loyalty in agribusiness by customer reward. Using facilities of Ethereum Network and solidity programing, we have created an Agri non-fungible token (NFT) according to ERC-721 protocol that is most widely used protocol for creating NFTs on Ethereum. One of the main advantages of ERC-721 is its flexibility, as it allows developers to define custom metadata for each NFT and customize the attributes. The interaction between Ethereum nodes and the user is established via the frontend, which is developed in Nodejs and connects to the Ethereum network. In previous works on loyalty systems, blockchain-based solutions were proposed that resemble the system developed in this study but there are significant differences between our work and other related works such as: the use of NFT and personalization for customers, the adoption of a new version of Solidity, as well as the adoption of the metadata feature for using media to encourage and assist customer brand awareness and repurchase. We tried to provide a safe and transparent way to use NFTs in the way of increasing brand loyalty in agricultural businesses. Due to characters of NFTs as uniqueness and personalization, customers in a loyalty cycle prefer to shop more in order to earn more AgriNFT, causing retailer to sell more items and manufacturers to generate more goods.
Operation and maintenance strategy of radio transmission equipment based on big data

Abstract In recent years, the operation and maintenance of radio transmission equipment have faced challenges such as equipment aging, high maintenance costs, long response time, and technological updates. It is necessary to adapt to new technologies and improve maintenance efficiency. The operation and maintenance strategy of radio transmitting equipment based on big data (BD) aims to optimize the reliability and operating efficiency of equipment through data analysis and intelligent algorithms, and reduce failure risk and maintenance costs. By analyzing the operation data and maintenance records of a large number of radio transmission equipment, it revealed the patterns and trends of equipment failures, in order to timely identify and predict possible faults, and take corresponding maintenance measures. This article adopted methods such as data collection and preprocessing, fault prediction and diagnosis, and maintenance optimization to achieve accurate monitoring of equipment operation status, fault prediction and diagnosis, optimized maintenance, and decision support. This can improve the operational efficiency and reliability of radio transmission equipment, which is used for fault diagnosis of radio transmission equipment through SVM (Support Vector Machine) algorithms. By training SVM classifiers, this article associated different types of faults with their feature data, thereby quickly and accurately determining the type of equipment fault based on real‐time collected device data, providing a basis for maintenance decision‐making. The experimental results show that the failure frequency of the radio transmitting equipment in the control group was between 7% and 11%, while the failure frequency of the radio transmitting equipment in the experimental group was between 3% and 7%. Experiments show that BD technology can effectively reduce the failure frequency of radio transmitting equipment. The empirical results indicate that this strategy can improve the reliability of equipment and reduce operation and maintenance costs, which has important guiding significance for the operation and maintenance management of radio equipment. This would bring about more efficient and reliable operation and maintenance management of radio equipment, making important contributions to the smooth progress of broadcasting business and the improvement of user experience.
CeTrivium: A Stream Cipher Based on Cellular Automata for Securing Real-Time Multimedia Transmission

Due to their significant correlation and redundancy, conventional block cipher cryptosystems are not efficient in encrypting multimedia data. Stream ciphers based on Cellular Automata (CA) can provide a more effective solution. The CA have recently gained recognition as a robust cryptographic primitive, being used as pseudorandom number generators in hash functions, block ciphers and stream ciphers. CA have the ability to perform parallel transformations, resulting in high throughput performance. Additionally, they exhibit a natural tendency to resist fault attacks. Few stream cipher schemes based on CA have been proposed in the literature. Though, their encryption/decryption throughput is relatively low, which makes them unsuitable for multimedia communication. Trivium and Grain are efficient stream ciphers that were selected as finalists in the eSTREAM project, but they have proven to be vulnerable to differential fault attacks. This work introduces a novel and scalable stream cipher named CeTrivium, whose design is based on CA. CeTrivium is a 5-neighborhood CA-based stream cipher inspired by the designs of Trivium and Grain. It is constructed using three building blocks: the Trivium (Tr) block, the Nonlinear-CA (NCA) block, and the Nonlinear Mixing (NM) block. The NCA block is a 64-bit nonlinear hybrid 5-neighborhood CA, while the Tr block has the same structure as the Trivium stream cipher. The NM block is a nonlinear, balanced, and reversible Boolean function that mixes the outputs of the Tr and NCA blocks to produce a keystream. Cryptanalysis of CeTrivium has indicated that it can resist various attacks, including correlation, algebraic, fault, cube, Meier and Staffelbach, and side channel attacks. Moreover, the scheme is evaluated using histogram and spectrogram analysis, as well as several different measurements, including the correlation coefficient, number of samples change rate, signal-to-noise ratio, entropy, and peak signal-to-noise ratio. The performance of CeTrivium is evaluated and compared with other state-of-the-art techniques. CeTrivium outperforms them in terms of encryption throughput while maintaining high security. CeTrivium has high encryption and decryption speeds, is scalable, and resists various attacks, making it suitable for multimedia communication.
Facial age progression: Review of scientific literature and value for missing person identification in forensic medicine

• The issue of missing persons is an international drama. • Age progression is used for the identification of missing person. • The possibility to recognize a person after years of missing represents a highly objective in forensic field.