Reviewerly/exHarmony huggingface dataset:
Blockchain Technology in the Digital Auditing Paradigm

The introduction of blockchain technology has ushered in a new era in the field of auditing. This study explores the multifaceted impact of blockchain technology on auditing practices, emphasizing its potential to revolutionize audit methodologies. The fundamental principles of blockchain and its implications in the auditing domain are explored extensively. Moreover, the chapter also discusses the role of auditors in navigating the complexities of blockchain. As the digital auditing landscape evolves, blockchain emerges as a powerful tool that promises to improve the credibility of financial reporting, making it imperative for professionals, academics, and regulators.
Hybrid Algorithms for Medical Insights Using Quantum Computing

In the field of medicine, machine learning has become very important. It's helping us identify diseases better, take care of patients more personally, and improve many important parts of healthcare. With more and more medical information available, machine learning, which relies on big sets of data, can make predictions more accurate and help doctors make better decisions. This chapter looks closely at how machine learning is being used in medicine right now. It also talks about how quantum computing could change healthcare. First, it explains different ways machine learning is used and how it's being used in medicine. It talks about things like diagnosing diseases, finding new medicines, and treating patients in a way that's right for them. Then, it explores how machine learning and quantum computing could work together. Quantum computing uses special particles to do many calculations at once. This could help process medical information much faster. Quantum computing could also help simulate how molecules interact, which is important for developing new medicines.
AI and Blockchain Fundamentals

This chapter explores the intersection of artificial intelligence (AI) and blockchain technologies in the realm of industrial robotics. The integration of AI and blockchain has the potential to revolutionize various aspects of the manufacturing sector, from enhancing robotic capabilities to optimizing supply chain management. This chapter delves into the key applications, benefits, challenges, and future prospects of combining AI and blockchain in industrial robotics. Both blockchain and artificial intelligence have generated a lot of excitement in the technology industry for a number of years. AI and blockchain applications for robotics applications thus provides productive, energy-efficient outcome and thus ease the process of automation and maintenance.
Implementation of Artificial Intelligence in Human Resource Management in Asia-Pacific

The amalgamation of pioneering technologies such as artificial intelligence in the human resources (HR) department has revolutionized the conduct of businesses nowadays. The inclusion of AI automates the organization's processes, benefiting the administration. Artificial neural networks, fuzzy logic, expert systems, data mining, machine learning, and genetic algorithms are a few of the techniques for the implementation of AI in HR. The chapter contemplates the role and benefits of artificial intelligence in organizations in the Asia-Pacific region to enhance the efficiency and effectiveness of human resource functions. It emphasizes the market players and use cases in the Asia-Pacific, illustrating the decision-making support solutions to deliver high-end value and profitability. This study also focuses on the trends and challenges in the implementation of AI in organizations to discover new potential and strategize for the future.
Application of AI and Blockchain Technologies in the Medical Domain

The rapid advancements in artificial intelligence (AI) and blockchain technologies have brought about significant transformations in various sectors, including healthcare. This proposal outlines a research paper that aims to investigate the different approaches and applications of AI and blockchain technologies in the medical domain. By exploring the integration of these technologies, the authors seek to analyze their potential benefits, challenges, and ethical implications in revolutionizing healthcare practices. The study will involve a comprehensive review of relevant literature, case studies, and interviews with experts in the field. The findings will contribute to a deeper understanding of the synergistic effects of AI and blockchain in healthcare, paving the way for enhanced decision-making, data security, interoperability, and patient-centric care.
Multi-Layered Blockchain-Based Security Model for Electronic Health Record Systems

The multi-layered blockchain-based electronic health records (ML-BbEHR) framework aims to address persistent healthcare issues such as data privacy and access control. Utilizing a multi-layered blockchain architecture, ML-BbEHR segments EHR data into categories like medical histories and clinical datasets, each managed within its distinct blockchain layer. This enhances security and optimizes data management. Unlike single-layer systems, this architecture mitigates latency issues. Additionally, the framework incorporates fingerprint authentication as an alternative to traditional cryptographic keys, bolstering patient identity verification. Smart contracts are also deployed across layers to delineate rules and permissions for healthcare data access, enhancing data integrity and security. This architecture is built on a general, non-specific blockchain platform chosen for its robust security features.
First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

Nonstationary fuzzy inference systems (NFIS) are able to tackle uncertainties and avoid the difficulty of type-reduction operation. Combining NFIS and neural network, a first-order sparse TSK nonstationary fuzzy neural network (SNFNN-1) is proposed in this paper to improve the interpretability/translatability of neural networks and the self-learning ability of fuzzy rules/sets. The whole architecture of SNFNN-1 can be considered as an integrated model of multiple sub-networks with a variation in center, variation in width or variation in noise. Thus, it is able to model both “intraexpert” and “interexpert” variability. There are two techniques adopted in this network: the Mean Shift-based fuzzy partition and the Group Lasso-based rule selection, which can adaptively generate a suitable number of clusters and select important fuzzy rules, respectively. Quantitative experiments on six UCI datasets demonstrate the effectiveness and robustness of the proposed model.
Genetic Algorithm-Based Method for Discovering Involutory MDS Matrices

In this paper, we present an innovative approach for the discovery of involutory maximum distance separable (MDS) matrices over finite fields <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><msub><mrow><mi mathvariant="double-struck">F</mi></mrow><mrow><msup><mrow><mn>2</mn></mrow><mrow><mi>q</mi></mrow></msup></mrow></msub></math> , derived from MDS self-dual codes, by employing a technique based on genetic algorithms. The significance of involutory MDS matrices lies in their unique properties, making them valuable in various applications, particularly in coding theory and cryptography. We propose a genetic algorithm-based method that efficiently searches for involutory MDS matrices, ensuring their self-duality and maximization of distances between code words. By leveraging the genetic algorithm’s ability to evolve solutions over generations, our approach automates the process of identifying optimal involutory MDS matrices. Through comprehensive experiments, we demonstrate the effectiveness of our method and also unveil essential insights into automorphism groups within MDS self-dual codes. These findings hold promise for practical applications and extend the horizons of knowledge in both coding theory and cryptographic systems.
Weighted meta-graph based mobile application recommendation through matrix factorisation and neural networks

Numerous mobile applications (apps) with different functions meet the various needs of users, but users have to spend a lot of time selecting suitable mobile apps. How to select relevant mobile apps for users has become an important issue. Existing studies mainly utilise context, user interest, privacy, security, version, and heterogeneous information to make mobile app recommendations. However, they have at least one of the following limitations: (1) Don't fully integrate the rich heterogeneous information; (2) Don't capture complex structural and semantic information; (3) Don't differentiate the importance of different semantic meta-graphs; (4) Don't consider the influence of different users' rating criteria. Therefore, the predictive performance of these methods is relatively limited. This paper considers the influence of different users' rating criteria for the same app and proposes a weighted meta-graph based mobile app recommendation approach by leveraging matrix factorisation and neural networks. Specifically, the similarity measurement between users and apps considers the difference in users' rating criteria under various semantic meta-graph patterns. The matrix factorisation technology is used to acquire the user's and the app's latent feature matrices. The importance of various semantic meta-graphs is distinguished by exploiting the weight learning. The neural network technology is employed to learn interactions between users and apps, thereby predicting the user's preference for unobserved apps. Experimental results demonstrate the superiority of the proposed approach, the effectiveness of considering differences in users' rating criteria, and the importance of differentiating various semantic meta-graphs.
Association between servant leadership and nurses’ turnover intention: evidence from Jordan

Nurses' turnover has remained a concern in the healthcare sector worldwide. Nurse managers should adopt appropriate leadership styles that promote a positive working environment. The literature revealed that effective leadership styles have lower turnover rates and higher job satisfaction among nurses in healthcare settings.This research aims to investigate whether a direct association exists between servant leadership and nurse turnover in public and private hospitals. While conventional leadership prioritises organisational success, servant leadership works on employee empowerment, growth and engagement, to secure success and trust among healthcare workers.A quantitative, cross-sectional study has been performed among 400 nurses from private and public hospitals in the northern parts of Jordan. A self-administered written survey was administered to those nurses in their departments. Structural equation modeling (SEM) using was used to analyse the data.Findings revealed that servant leadership had a negative direct impact on nurse turnover intention. Servant leadership prioritises employee empowerment of nurses in their workplaces.Healthcare agencies and healthcare professional organisations could use the study findings to understand better what influences nurses' decisions and behaviours and what causes them to resign. Further findings of this study may assist nurse managers in developing appropriate retention strategies and reducing the likelihood of nurses resigning.
Constructive theories through a modal lens

Abstract We present a uniform proof-theoretic proof of the Gödel–McKinsey–Tarski embedding for a class of first-order intuitionistic theories. This is achieved by adapting to the case of modal logic the methods of proof analysis in order to convert axioms into rules of inference of a suitable sequent calculus. The soundness and the faithfulness of the embedding are proved by induction on the height of the derivations in the augmented calculi. Finally, we define an extension of the modal system for which the result holds with respect to geometric intuitionistic.
Digital competence of the information society in the aspect of social security

Objectives The purpose of the digital competency survey is to assess the skills, knowledge and attitudes of the information society in the use of digital technologies and the development of these skills. As a result of the survey, the level of digital competence of the public was determined, and training needs and activities were identified to increase digital knowledge and skills in order to improve quality of life, achieve better performance at work and increase competitiveness in the labor market. Material and methods The purpose of the survey is to assess the digital skills, knowledge and attitudes of the information society and the development of these skills. The survey determines the level of digital competence of the public and identifies training needs and activities to increase digital knowledge and skills to improve quality of life. Results The survey's conclusions point to the need for increased education and awareness of online security in organizations, especially in the areas of privacy and data security. It is therefore worth betting on developing users' knowledge of online security. Conclusions Communication and information: respondents gained more experience and were more aware of the benefits of digital competence in these areas. Creativity: although some improvement was observed in this area, there is still a significant gap between current and expected levels of competence. Digital security: this area deserves special attention, as the current level of competence has declined, despite rising expectations. Troubleshooting: despite a slight increase in current competencies in 2021, a decline in 2022 indicates that respondents may be struggling to solve problems in a digital context.
Relations between information science and information security culture

Objectives The aim of the article is to present the relations that exist between the culture of information security and information sciences. Material and methods desk research, Literature criticism, Synthesis, Comparative analysis. Results The ability to manage information correctly is essential from the point of view of security. As a result, one may conduct research with the aim of developing optimised actions concerning information management and determine the borders between the security of information and informational security. In this aspect, the scientific considerations should also include studies of the relations that exist between information management and information security management. In order to manage security efficiently, it is necessary to understand the nature of security and to know the current challenges and threats. Conclusions In a world where IT tools are an indispensable element of daily human activities, information science has a strong influence on the information security culture. These areas of activity mainly include the development of security technologies, where information science strives to develop systems that would effectively protect users from threats such as distortion or data loss.. Another sphere is education for employees about information security, which would help develop appropriate models for an information security culture. Another important aspect is the human awareness of threats that may be encountered during the use of IT tools or platforms. The final aspect that should be considered is risk management. Information science enables analysis to be conducted regarding risks related to information security and to treat them as the basis for developing optimum action plans in the event of an emergence of threat.
Research on Key Technologies of Intelligent Defect Image Detection for Substation Equipment in Complex Scenes

Image feature matching and image recognition are two important branches of image processing. In recent years, with the rapid development of artificial intelligence, big data and other information technology, as well as the technological breakthrough of computer computing ability, image analysis technology has been widely used in various industries. Daily inspection and defect detection of substation equipment is an important work of equipment operation and maintenance. Relying on image intelligent processing technology, it can greatly improve the efficiency of operation and maintenance, reduce the workload of operation and maintenance personnel, improve the detection probability of equipment defects and abnormalities, and ensure the safe and stable operation of power grid. Aiming at the complex defect scene of substation equipment, it has good research significance and application prospect to carry out the research of intelligent defect image detection technology. This paper described the basic principles and development of image feature matching technology and artificial intelligence image recognition technology, discussed the key problems and technical difficulties in the practical application of image processing technology, introduced the solutions and technical characteristics of key technologies, and put forward the image intelligent detection algorithm and multi-dimensional image comprehensive analysis method suitable for complex scenes. The verification results indicated that the method described in the article can effectively solve the problem of device multi scene defect detection.
Automatically generated language learning exercises for Finno-Ugric languages

Morphologically rich languages always constitute a great challenge for language learners. The learner must be able to understand the information encoded in different word forms of the same root and to generate the correct word form to express certain syntactic functions and grammatical relations by conjugating a verb or declining a noun, an adjective or a pronoun. One way to improve one’s language skills is through exercises that focus on certain aspects of grammar. In this paper, a language learning application is presented that is intended to help learners of Finnish and Hungarian (with Hungarian and Finnish L1, respectively) acquire new vocabulary items, as well as practice some grammar aspects that according to surveys are considered difficult by learners of these languages with the other Finno-Ugric language being the learner’s native tongue, while alleviating the need to create these exercises manually. This application is a result of an on-going research project. In this research project, bilingual translation pairs and additional monolingual data were collected that can be utilized to build language learning exercises and an online bilingual dictionary with the help of automatic methods. Several linguistic patterns and rules were defined in order to automatically select example sentences that focus on a given part of the target language. These sentences were automatically annotated with the help of language processing tools. Due to the large size of the previously collected data sets, to date, only a subset of the analyzed sentences and the bilingual translation pairs has been manually evaluated. The results of this evaluation are discussed in this paper in order to estimate the precision of the methodology presented here. To ensure the precision of the information and the reliability of the application, only manually validated data sets are displayed. In this project, continuous data validation is planned, since it leads to more and more examples and vocabulary items that learners can benefit from.
Is a quantifier mismatch a problem for L1 Japanese learners of English?

After identifying a linguistic difference between the English quantifier most and the Japanese quantifier hotondo ‘most’ we set out to find if the semantic difference between the two would constitute a learning problem for Japanese second language (L2) learners of English. The difference we hypothesized between the two is that English most is considered “more than half,” while hotondo is “nearly all.” As this semantic difference is not explicitly taught in a classroom environment, acquisition by learners would need to take place through experiencing most in contexts where they might receive contextual clues. An examination of a corpus indicated that contextual clues towards such a semantic difference would be unavailable or rarely available. Two sets of experiments (Experiments 1 and 2) were conducted using the Truth Value Judgment Task methodology. The results of Experiment 1 showed that L2 speakers treated most as meaning “nearly all” but that the level of learner proficiency has an effect. The upper intermediate L2 learner group (Experiment 1a) behaved more like the L1 English speaker group (Experiment 1b) than the lower proficiency L2 group (Experiment 1c). Experiment 2, testing Japanese L1 speakers on their interpretation of Japanese hotondo ‘most,’ revealed that while a majority of participants treated hotondo as “almost all,” there was, somewhat unexpectedly, a group of speakers who interpreted hotondo to mean “more than half.” Therefore, although the possibility cannot completely be eliminated that the result of Experiment 1a is due to L1 transfer, if some Japanese learners of English can unlearn the incorrect meaning, then some prior, if not innate, knowledge that makes the process possible must be available to them.
Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery

Vehicle detection and tracking from unmanned aerial vehicles (UAVs) aerial images are among the main tasks of intelligent traffic systems. Especially in tasks with long distances, extensive backgrounds, and small objects, it increases the difficulty of localization and regression, which can easily lead to missed detections and false positives. This paper proposes a detection-based small-scale vehicle tracking framework that integrates an improved YOLOX network and the DeepSORT algorithm to address these issues. Based on the original YOLOX network, a shallow feature extraction network, 160 × 160 pixels, is added to enhance the ability to extract small-scale object features. A convolutional block attention module (CBAM) is inserted in front of the neck network to select crucial information for vehicle detection tasks while suppressing noncritical ones. EIoU_Loss is introduced as the bounding box regression loss function in training to speed up their convergence and improve the localization accuracy of the small objects. Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. Finally, the improved YOLOX network is used as the detector of the DeepSORT to perform small-scale vehicle detection and tracking tasks in various traffic scenarios. Experiments show that the proposed method can significantly improve the detection accuracy of the network and effectively solve the problems of missed detection and false positives in small-scale vehicle tracking tasks in high-resolution aerial images captured by high-altitude UAVs. Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes.
Design for Behavior Change in Design Education. A Case Study

Abstract Addressing design education programs according to the goals for sustainable development indicated by the ONU Agenda 2030 requires the update of contents and methodologies. The paper deals with why design theories for behavior change (DfBC) should be considered in design education at the university level, with a particular focus on service and interaction design. It also reports, as a case study, the results of including DfBC in a UX Design course, where students were engaged in the design of solutions aimed at supporting change of behaviors for sustainability. The experiment shows the potential of these theories as valuable educational content; it also enlights the need to develop further ethical discussion on the use of brain sciences in design.