Reviewerly/exHarmony huggingface dataset:
Blockchain in Healthcare

Blockchain technology has emerged as a solution to tackle challenges in the healthcare sector, such as fragmented data vulnerabilities in security and a lack of transparency. In this chapter, the authors provide an overview of blockchain and its potential benefits for the healthcare industry. These include collaboration, improved integrity and security of data, enhanced efficiency, empowerment of patients, and advancements in supply chain management. However, implementing blockchain faces obstacles like scalability issues, the absence of interoperability standards uncertainties regarding regulatory aspects, and usability concerns. To ensure the adoption of technology in healthcare, the authors propose a strategic roadmap for both short-term implementation at individual hospitals and long-term transformation of insurance and billing processes through smart contract automation. This requires integrating with existing IT systems training for staff members addressing ethical considerations effectively and customizing approaches based on regional requirements.
MTLink: Adaptive multi-task learning based pre-trained language model for traceability link recovery between issues and commits

Traceability links between issues and commits (issue-commit links recovery (ILR)) play a significant role in software maintenance tasks by enhancing developers’ observability in practice. Recent advancements in large language models, particularly pre-trained models, have improved the effectiveness of automated ILR. However, these models’ large parameter sizes and extended training time pose challenges in large software projects. Besides, existing methods often overlook the association and distinction among artifacts, leading to the generation of erroneous links. To mitigate these problems, this paper proposes a novel link recovery method called MTLink. It utilizes multi-teacher knowledge distillation (MTKD) to compress the model and employs an adaptive multi-task strategy to reduce information loss and improve link accuracy. Experiments are conducted on four open-source projects. The results show that (i) MTLink outperforms state-of-the-art methods; (ii) The multi-teacher knowledge distillation maintains accuracy despite model size reduction; (iii) The adaptive multi-task tracing method effectively handles confusion caused by similar artifacts and balances each task. In conclusion, MTLink offers an efficient solution for ILR in software traceability. The code is available at https://zenodo.org/records/10321150.
Artificial intelligence capabilities, dynamic capabilities and organizational creativity: contributing factors to the United Arab Emirates Government’s organizational performance

Purpose This study aims to assess the effectiveness of a scale measuring artificial intelligence capabilities by using the resource-based theory. It seeks to examine the impact of these capabilities on the organizational-level resources of dynamic capabilities and organizational creativity, ultimately influencing the overall performance of government organizations. Design/methodology/approach The calibration of artificial intelligence capabilities scale was conducted using a combination of qualitative and quantitative analysis tools. A set of 26 initial items was formed in the qualitative study. In the quantitative study, self-reported data obtained from 344 public managers was used for the purposes of refining and validating the scale. Hypothesis testing is carried out to examine the relationship between theoretical constructs for the purpose of nomological testing. Findings Results provide empirical evidence that the presence of artificial intelligence capabilities positively and significantly impacts dynamic capabilities, organizational creativity and performance. Dynamic capabilities also found to partially mediate artificial intelligence capabilities relationship with organizational creativity and performance, and organizational creativity partially mediates dynamic capabilities – organizational creativity link. Practical implications The application of artificial intelligence holds promise for improving decision-making and problem-solving processes, thereby increasing the perceived value of public service. This can be achieved through the implementation of regulatory frameworks that serve as a blueprint for enhancing value and performance. Originality/value There are a limited number of studies on artificial intelligence capabilities conducted in the government sector, and these studies often present conflicting and inconclusive findings. Moreover, these studies indicate literature has not adequately explored the significance of organizational-level complementarity resources in facilitating the development of unique capabilities within government organizations. This paper presents a framework that can be used by government organizations to assess their artificial intelligence capabilities-organizational performance relation, drawing on the resource-based theory.
Motion-Based Wave Inference With Neural Networks: Transfer Learning From Numerical Simulation to Experimental Data

Abstract The directional wave spectrum, which describes the distribution of wave energy along frequencies and directions, can be estimated from the measured motions of a vessel subjected to a particular sea condition by resorting to the wave-buoy analogy. Several methods have been proposed to address the inverse estimation problem; recently, machine learning techniques have been assessed as further alternatives. However, it may be difficult to gather large datasets of in-service motion responses and the associated sea states to train effective data-driven models. In this work, an encoder–decoder neural network is trained with the synthetic responses of a station-keeping platform supply vessel (PSV) to estimate the directional wave spectrum. This estimation model is directly applied to perform wave inference from motion data of wave basin tests with a small-scale model of the same vessel. Furthermore, fine-tuning is also used to incorporate experimental data into the neural network model. Results show a satisfactory match between estimated and measured values, both with respect to the energy distribution and the integral spectrum parameters, indicating that the proposed approach can be employed to obtain data-driven wave inference models when there is little or no availability of measured motion records and the corresponding sea conditions.
Implementing quantum anonymous multi-party ranking: the influence and application of the law of large numbers

Abstract Secure multi-party ranking is not only a pivotal component within the domain of secure multi-party computation but also holds extensive real-world applicability, given its efficacy in safeguarding the integrity and privacy of data. In this paper, we propose a verifiable quantum anonymous multi-party ranking protocol, which leverages the power of multi-particle entangled states in conjunction with the law of large numbers. The protocol achieves the secure ranking of participants’ private data while verifying their identities through the assistance of a semi-honest third party. We prove that the protocol is resistant to different types of attacks from internal or external attackers. In addition, the proposed protocol employs qubits as the information carriers, which improves the feasibility of the protocol. We demonstrate the feasibility of the protocol by using the online analog quantum computer of IBM Corporation placed on the cloud. Our research represents an innovative initiative that merges the realms of quantum cryptography and statistical analysis to address pertinent practical challenges.
A lightweight multi-target ship tracking model based on Yolov7

Abstract Ship multi-target tracking has long been a topic that has attracted researchers from different fields. Currently, the following challenges still exist in ship multi-target tracking. Ships obscuring each other leads to an increase in misdetections and omissions in the algorithm. Some algorithms have a large number of parameters and computations, which are not favourable for deployment into devices. As a result, we present a novel multi-target tracking technique that combines the Yolov7 detector with Kalman filtering. First, the retrieval of fine ship details in the video is accomplished by employing the CNNS(Convolutional Neural Networks) + Transformer + CNNS architecture. Second, we introduce a novel lightweight module known as Light-SPP, which aims to integrate ship features. Finally, Wise-iou Loss, which increases the predictability of ship position, is shown as the detector’s localization loss function. The experimental results show that the number of parameters and computation of the model decrease by 11.0% and 17.7% respectively, and the continuous tracking accuracy and tracking and positioning accuracy improve by 17.7% and 7.4% respectively. In summary, our proposed ship multi-target tracking algorithm mainly solves the problems of low tracking accuracy and large computational volume, and has high engineering application prospects in the field of water transport.
A robust multi-chaotic remote sensing image encryption scheme based on RNA and immune algorithms

Abstract Remote sensing images have been widely used in the military and other areas because of their rich perceptional data. This makes their visual security critical for practical usage. To address this challenge, an enhanced image encryption scheme is proposed. In the scrambling phase, n bands of remote sensing images undergo Arnold double-bit-level permutation. This reduces not only the pixel correlation in each image plane but also between each frequency band.To enhance security, an RNA crossover rule (RNACMO) is introduced. The RNA image is divided into RNA single strands of different lengths using chaotic sequences, and different crossover methods, including single-point and uniform, are adaptively selected according to the number of RNA single strands. RNACMO significantly improves the security level of the scheme. An improved immune algorithm (IIA) is exploited to optimize chaotic function sequences, which improves the chaotic property of the scheme. In experiments, the proposed algorithm achieves average values of 99.6094% for NPCR, 33.4635% for UACI, and 26.7712% for BACI in encrypted remote sensing images, indicating stronger security and better resilience against attacks compared with other encryption algorithms for remote sensing images.
Development of QR Code

QR code, an abbreviation for Quick Response code, was developed and published three decades ago. It was originally designed for production and logistics industries with advantageous characters: 1) high-speed readability, 2) flexibility for reading angles, and 3) robust readability against noise. In this paper, structure of code and encoding/decoding process will be introduced.
How to Measure Cybersecurity and Why Heuristics Matter

In this article, we continue our lessons learned from medical science and explore possibilities to measure cybersecurity and apply heuristics.
Open Science for Fostering Seismologic Science Literacy

This paper reports on an attempt to introduce “open science” into the field of seismology to improve disaster literacy in seismology and related fields. The term “open science” has a variety of meanings, but the core idea is of diverse stakeholders “doing science collaboratively.” This study reports on four pieces of action research to improve seismology-related disaster literacy through the idea of “open science,” which focuses on “doing science collaboratively” with the active participation of citizens (non-experts) and researchers (experts). In particular, this study focuses on the “citizen science” perspective because disaster literacy, first and foremost, refers to the disaster literacy of the general public. Specifically, we improved earthquake literacy by operating the Abuyama Earthquake Observatory Science Museum; promoting citizen-participatory earthquake and tsunami evacuation drills by developing and implementing Nige-tore, a smartphone app to support tsunami evacuation drills; promoting the Minna de Honkoku project to decipher historical materials related to natural disasters through citizen involvement; and conducting a trial “open/citizen science”-oriented seismologic-data observation program, the Manten Project, for inland earthquake research.
Open-Door Events of the Sakurajima Volcano Observatory, Kyoto University in the Last Ten Years

Sakurajima Volcano Observatory, with a history exceeding 60 years, is recognized by researchers in Japan and overseas for conducting frontier research centering on Sakurajima, the most active volcano in Japan. It is owned by Kyoto University and represents the university in the surrounding communities. This paper reviews the open-door events, which have been conducted yearly for the last ten years as a part of a university-wide effort by Kyoto University. They make use of the facilities at Sakurajima Volcano Observatory, which aims to enhance the understanding of volcanoes among the general public in collaboration with geo-guides as “boundary workers.”
Enhancing willingness to communicate in English among Chinese students in the UK: the impact of MALL with Duolingo and HelloTalk

Abstract Over the past decade, mobile-assisted language learning technologies, such as smartphone applications, have become increasingly prominent in language learning; however, empirical research on how the use of mobile apps can influence students’ willingness to communicate (WTC) is scarce. This study assessed two mobile apps with distinct language learning approaches: Duolingo, grounded in grammar-translation and behaviourist principles, and HelloTalk, based on social constructivist theory and communicative language learning. The research examined the impact of these apps on Chinese students’ WTC in the UK. A mixed-methods approach, including a quasi-experimental design, was adopted, involving 67 International Foundation Year Chinese students from a Confucius Heritage Culture background. The participants were randomly assigned to two experimental groups (Duolingo, N = 33; HelloTalk, N = 34) and used one app for five weeks. Quantitative data were collected through pre- and post-treatment using a WTC scale and an English media usage frequency questionnaire. Qualitative data were obtained through semi-structured interviews. Results indicated that both apps enhanced WTC, with Duolingo exhibiting a slightly stronger effect. Participants displayed greater WTC with emotionally and socially connected individuals, while WTC with strangers remained lowest. Factors such as usage time, communication goals, and frequency of use influenced participants’ WTC with various groups of people.
Embracing intelligent machines: A qualitative study to explore the transformational trends in the workplace

Purpose With Industry 4.0 and the extensive rise of smart technologies, we are seeing remarkable transformations in work practices and workplaces. Scholars report the phenomenal progress of smart technologies. At the same time, we can hear the rhetoric emphasising their potential threats. This study focusses on how and where intelligent machines are leveraged in the workplace, how humans co-working with intelligent machines are affected and what they believe can be done to mitigate the risks of the increased use of intelligent machines. Design/methodology/approach We conducted in-depth interviews with 15 respondents working in various leadership capacities associated with intelligent machines and technologies. Using NVivo, we coded and churned out the themes from the qualitative data collected. Findings This study shows how intelligent machines are leveraged across different industries, ranging from chatbots, intelligent sensors, cognitive systems and computer vision to the replica of the entire human being. They are used end-to-end in the value chain, increasing productivity, complementing human workers’ skillsets and augmenting decisions made by human workers. Human workers experience a blend of positive and negative emotions whilst co-working with intelligent machines, which influences their job satisfaction level. Organisations adopt several anticipatory strategies, like transforming into a learning organisation, identifying futuristic technologies and upskilling their human workers, regularly conducting social learning events and designing accelerated career paths to embrace intelligent technologies. Originality/value This study seeks to understand the emotional and practical implications of the use of intelligent machines by humans and how both entities can integrate and complement each other. These insights can help organisations and employees understand what future workplaces and practices will look like and how to remain relevant in this transformation.
Intelligent System Detection of Dead Victims at Natural Disaster Areas Using Deep Learning

Disaster is the occurrence or sequence of occurrences that endangers and disrupts people’s lives and livelihoods due to natural and/or non-natural as well as human elements, including fatalities, property loss, environmental harm, and psychological effects. In addition to concentrating on the victims’ safety and their own safety, the search and rescue (SAR) team plays a significant part in this evacuation operation. Based on these issues, this study examined how to use a drone equipped with electronic equipment to search for victims on the ground to speed up the evacuation process at natural disaster sites, assisting the evacuation process and enhancing the safety of the SAR team. The drone carries a near-infrared camera and GPS. The images captured by the camera provide the parameters for classifying victims using deep learning. The system has been implemented by sampling data from human poses resembling the position of the victims’ bodies from natural disasters. From the experimental results, the system can detect objects with high accuracy, that is, 99% in both static and dynamic conditions. The best model results were obtained at a height of 2 meters with a low error percentage.
GISPLIT: High-performance global solar irradiance component-separation model dynamically constrained by 1-min sky conditions

The separation of global horizontal irradiance (GHI) into its direct and diffuse components is necessary in a variety of applications, most specially in solar energy utilization, where knowledge of direct normal irradiance (DNI) is of paramount importance. Here a novel and efficient model, referred to as GISPLIT, is presented to perform this task accurately, using time series of measured data at 1-min resolution. To better describe the radiative effects of different cloud situations, the model takes advantage of a preliminary classification of the sky conditions into six sky types. An empirical submodel is assigned to each sky class to split GHI into its components, using a limited number of predictors that are related to GHI’s magnitude and variability, and to coincident estimates of the clear-sky irradiance components. Those submodels are trained and validated using rigorously quality-assessed measurements from 120 radiometric stations over all continents and all five major Köppen-Geiger (KG) climate classes, totaling ≈64 million valid data points. Four model versions are evaluated using training data for either all KG climate regions combined or conditioned by KG climate, and either with or without additional support from machine learning. The validation of the four versions suggests that the conditioning by KG climate does not add any significant benefit over the “all-climates” training approach and that, overall, the model version trained with data from all KG climates combined and supported by machine learning generally predicts DNI with the best RMSE results at unseen sites, although with little difference over the other versions.
Exploring the uncharted territories: a structured literature review on cryptocurrency accounting and auditing

Purpose The objective of this study is to systematically review the current body of literature in order to gain insights into the progress of research in accounting and auditing of cryptocurrencies, while also highlighting the associated risks and identifying gaps for future exploration. Design/methodology/approach To achieve this, a structured literature review was carried out, presenting a thorough and critical assessment of the available studies focused on cryptocurrencies within the accounting and auditing domain. Findings The analysis reveals that the majority of the research has concentrated on the reporting and measurement aspects of cryptocurrencies, neglecting the auditing aspect. Regarding the methodology, future investigations should incorporate both theoretical and empirical manners to address this gap. Various spheres require further exploration, as they have the potential to significantly impact practitioners and academics. Originality/value The significance of this paper lies in its comprehensive examination of the existing literature, synthesizing and organizing information pertaining to accounting and auditing considerations of crypto transactions. Moreover, it provides valuable insights into best practices and prompts identifying avenues for further research in this field.
Digital transformation and integration of artificial intelligence in financial institutions 

Purpose Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience. Design/methodology/approach The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots. Findings The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions. Originality/value The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.