Reviewerly/exHarmony huggingface dataset:
Secondary education and technological innovation: educational tools for modern learning

This study addressed the impact of technological innovation in high school and the implementation of modern educational tools. The central problem investigated was the effective integration of these technologies into the educational environment and their effect on student performance and engagement. The overall objective was to analyze the impact of these innovations, identify the most effective tools, and explore inclusive and equitable implementation practices. The methodology adopted was a literature review, allowing for a comprehensive analysis of the topic. The results indicated that despite challenges related to equity of access and resistance to change, educational technologies offer significant opportunities to revitalize teaching and learning. The analysis highlighted the need for a holistic approach in adopting technologies, emphasizing the transformation of pedagogical practices and educator training. The final considerations emphasized the potential of technological innovations to enrich the educational experience, provided they are accompanied by appropriate pedagogical strategies and equitable accessibility.
Deep investigation of muography in discovering geological structures in mineral exploration: a case study of Zaozigou gold mine

Summary Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements, and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analyzed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel three-dimensional reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefiting from the seed algorithm, a low-density-contrast orebody and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field.
Empirical analysis of optimized portfolio allocation based onMarkowitz and index models

There is a need to achieve a balance between asset returns and risks. This has remained a central focus of financial market research. It has served as a crucial reference for investment decision-making. There has been a weakness as this financial investment theory relies on qualitative analysis. This method has lacked robust quantitative methods. The resurgence and expansion of Western economies have led to a flourishing financial investment activities. This has prompted the emergence of the Modern Portfolio Theory (MPT). The theory was pioneered by Harry Markowitz in 1952. MPT has rapidly evolved over the period. It has attracted numerous scholars and yielded substantial research outcomes (Markowitz, 1991).
A case study on digital training: benefits and unexplored opportunities

Abstract In an era characterized by the increasing complexity of products and the rapid turnover of the workforce across different companies, there is a growing need to invest significantly in quick and efficient training methods. Concurrently, the advancement of digitalization has rendered certain training practices anchored to paper-based materials obsolete. Numerous companies are directing their investments toward digital training, yet the optimal format to exploit the full advantages of digitalization remains unclear. This study undertakes a comparison of four distinct digital versions of the same training process with the aim of comprehending the tangible benefits. The findings indicate that to fully capitalize on the advantages of digital technology, a complete rethinking of training practices is necessary.
The attribute classification model in intelligent question answering system based on domain knowledge graph

The knowledge graph-based question answering system aims to find the connection between the internal knowledge behind the problem and the known knowledge base triplet. This paper proposes a framework for knowledge graph question answering (ZDNN-KGQA), which divides intelligent question answering based on knowledge graphs into four steps: named entity recognition, entity disambiguation, attribute classification, and answer selection. In the named entity recognition step, this paper proposes a BERT-BiLSTM-CRF model that combines an attention mechanism to identify entities in question sentences. In the entity disambiguation step, this paper utilizes the BERT-Softmax model to extract the accurate entities from the candidate entity set. In the attribute classification step, it is converted into a problem of text similarity. This paper proposes the use of BERT-Softmax and Word2vec models to extract attributes intext problems, effectively improving entity recognition performance and question semantic parsing performance. The average F1 value of this system on the test dataset provided by the NLPCC-ICCPOL 2016 KBQA task is 0.8974, which is close to the optimal performance level.
Pengembangan Sistem Informasi Manajemen Kepegawaian pada Yayasan Pondok Pesantren Nurul Huda Sukaraja

Along with advances in information technology, the Nurul Huda Islamic Boarding School Foundation still relies on managing personnel data manually without adopting a personnel management information system. This research aims to design a personnel information system that includes employee data management, including rank, leave applications, attendance, further studies, and monitoring of employee performance. In this research, the methodology used is Extreme, which is used to model process flow. In contrast, Data Flow Diagrams (DFD) are used to model data flow in a system, helping to clearly describe processes, data, and interactions between system components. The system development method used is extreme programming. Then, the system will be tested using the Black Box Testing method. The results show that the system is 100% valid and 100% compatible.
Decoding 2.3 Million ECGs: Interpretable Deep Learning for Advancing Cardiovascular Diagnosis and Mortality Risk Stratification

Abstract Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. Utilising a dataset of 2,322,513 ECGs collected from 1,558,772 patients with 7 years of follow-up, we developed a deep learning model with state-of-the-art granularity for the interpretable diagnosis of cardiac abnormalities, gender identification, and hyper- tension screening solely from ECGs, which are then used to stratify the risk of mortality. The model achieved the area under the receiver operating characteristic curve (AUC) scores of 0.998 (95% confidence interval (CI), 0.995-0.999), 0.964 (0.963-0.965), and 0.839 (0.837-0.841) for the three diagnostic tasks separately. Using ECG-predicted results, we find high risks of mortality for subjects with sinus tachycardia (adjusted hazard ratio (HR) of 2.24, 1.96-2.57), and atrial fibrillation (adjusted HR of 2.22, 1.99-2.48). We further use salient morphologies produced by the deep learning model to identify key ECG leads that achieved similar performance for the three diagnoses, and we find that the V1 ECG lead is important for hypertension screening and mortality risk stratification of hypertensive cohorts, with an AUC of 0.816 (0.814-0.818) and a univariate HR of 1.70 (1.61-1.79) for the two tasks separately. Using ECGs alone, our developed model showed cardiologist-level accuracy in interpretable cardiac diagnosis, and the advancement in mortality risk stratification; In addition, the potential to facilitate clinical knowledge discovery for gender and hypertension detection which are not readily available.
On logistic regression versus support vectors machine using vaccination dataset

The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.
AUGMENTED REALITY ADVERTISEMENTS WITHIN THE REALM OF PHYGITAL MARKETING: CASE STUDIES OF ADVERTISING EXAMPLES

Technology has evolved to be a concept of constant development and innovation, significantly impacting both individual and societal life. It has become a fundamental building block that influences personal inclinations, choices, and decisions, and is already present in many phases of human life. Individuals react to technological advancements they encounter in their daily lives, as they are not just consumers in the current era, but also producers. The responses to technological advancements extend to influencing purchasing activities and shopping behaviors of individuals. This study has been developed to analyze the role and usage of Augmented Reality technology, one of these technological advancements, in the transforming field of marketing and specifically in advertising. The evolution of the marketing concept has brought about new approaches, as is the case in almost every developing field. Among these new approaches, the influence of digitalization is acknowledged. Within these definitions, the concept of phygital marketing is included. The phenomenon of transferring real-life actions to a virtual (digital) environment has given way to the transfer of virtual (digital) environments into the physical, real-life aspect. The term "phygital", a combination of physical and digital, can be summarized as the reflection of digital phenomena, especially in the physical dimension, into everyday life. One of the application areas where the impact of phygital concept in marketing is evident is the use of Augmented Reality technology in advertisements, which defines the scope of this study
Autonomous collision avoidance method based on improved artificial potential field in the traffic separation scheme waters

In order to realize the autonomous navigation of ships in the complex waters under the traffic separation system (TSS), taking the Chengshanjiao TSS waters as an example, an improved autonomous collision avoidance algorithm is proposed based on the artificial potential field (APF) method. By establishing five kinds of potential fields and their force functions, including the gravitational function of the goal point, the repulsion function of the target ships (TS), the gravitational function of the centerline of the traffic lane, the velocity function of the ship, and the boundary repulsion function of the navigation lane, finally the resultant force of them is calculated to guide the navigation direction of the ship, during the navigation process in this TSS water. Multiple groups of comparative experiments demonstrated that under the improved algorithm, the ship could avoid collisions with TS in four typical scenarios, according to the requirements of the 1972 International Regulations for Preventing Collisions at Sea (COLREGS) and good seamanship practice, simultaneously the ship could proceed in the navigable lane and keep safe distance with the boundaries and separation zones. The method proposed in this paper is expected to provide basic theoretical basis and technical support for the safe navigation of autonomous ships in TSS waters.
A study on the correlation between the English score of collegeentrance Examination and the score of the first CET-4

English is the most widely used language in the world. Nowadays, the world is showing a trend of globalization. As a bridge connecting us with the world, the importance of English is self-evident. The study of high school is to let us master the ability to use English communication and to strengthen our ability to use English further when we enter the university. The college entrance examination is a standard reference examination for high school students in China after completing the basic learning stage. Its purpose is to comprehensively check whether the students who have completed the high school courses have met the requirements stipulated in the syllabus. After years of practice, it has become one of the most authoritative examinations in China. [1] In recent years, many colleges and universities have paid great attention to the CET-4 test, and most have linked the CET-4 test with a bachelor’s degree. Students cannot get the CET-4 certificate in universities, and the schools will not award bachelor’s degrees to the students. China’s College entrance examination and CET-4, as two of the most objective level examinations in China, have strong reliability and validity in the field of English learning and have a certain continuity over the years, which can objectively and honestly reflect the English learning level of students from high school to college. [2] The English score of the college entrance examination can also reflect the English level of first-year college students when they first enter school. While CET-4 represents a student’s English level at the university level.
Scalable measurement error mitigation via iterative bayesian unfolding

Measurement errors are a significant obstacle to achieving scalable quantum computation. To counteract systematic readout errors, researchers have developed postprocessing techniques known as measurement error mitigation methods. However, these methods face a tradeoff between scalability and returning nonnegative probabilities. In this paper, we present a solution to overcome this challenge. Our approach focuses on iterative Bayesian unfolding, a standard mitigation technique used in high-energy physics experiments, and implements it in a scalable way. We demonstrate our method on experimental Greenberger-Horne-Zeilinger state preparation on up to 127 qubits and on the Bernstein-Vazirani algorithm on up to 26 qubits. Compared to state-of-the-art methods (such as M3), our implementation guarantees valid probability distributions, returns comparable or better-mitigated results, and does so without a noticeable time and memory overhead.
Magic behaviors in non-Markovian environment

Magic as an essential resource in fault-tolerant quantum computation may shed insight into quantum information processing of open quantum system. In this work, we study the dynamics of magic for atom system immersed in non-Markovian reservoir. It is found that magic decay process can be delayed and adjusted by the strength of classical driving field and non-Markovian memory effect. In addition, it is shown that the magic state is less susceptible to decay the process of system-environment coupling than stabilizer state, and for two atoms coupling to two independent reservoirs, magic can be generated. The results may be beneficial for theory and experiment of quantum computation and information in open quantum system.
A Proposal of Naturalistic Software Development Method

Naturalistic programming purports to include natural language elements in programming languages to increase software expressiveness. Even though natural language is inherently ambiguous, it is richer and thus more expressive than any artificial language. Currently, the Naturalistic Programming Paradigm (NPP) is supported by its conceptual model and three general-purpose naturalistic programming languages that can generate executable binary code. Nevertheless, to date, no research efforts have been concentrated on applying the NPP within a software development process. To address this gap, in this article, we propose a naturalistic software development method to test the advantages of the NPP. The method focuses on the analysis and design stages of the software development process and seeks to contribute to closing the gap between the problem and the solution domains. We also present an example of an implementation using Cal-4700, a naturalistic programming language, showing the differences in expressiveness of programming with a traditional programming language, like Python.&amp;nbsp;
Cascaded PFLANN Model for Intelligent Health Informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

Due to the recent developments in communications technology, cognitive computations have been used in smart healthcare techniques that can combine massive medical data, Artificial intelligence, federated learning, Bio-inspired computation, and the Internet of Medical Things. It has helped in knowledge sharing and scaling ability between patients, doctors, and clinics for effective treatment of patients. Speech-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising results. Since the subject’s speech can be remotely recorded and submitted for further examination, it offers a quick, economical, dependable, and non-invasive prospective alternative detection approach. However, the two main requirements of this are higher accuracy and lower computational complexity and, in many cases, these two requirements do not correlate with each other. This problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy. A Cascaded Perceptual functional link artificial neural network (PFLANN) is used to capture the non-linearity in the data for better classification performance with low computational complexity. The proposed model is being tested for multiple respiratory diseases and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.
Optimasi Centroid Awal Algoritma K-Medoids Menggunakan Particle Swarm Optimization Untuk Segmentasi Customer

Customer segmentation is an important strategy in a company, it affects good customer relationships which will result in increased profits. Grouping customers in data mining can use several algorithms, but K-Medoids is the right choice because it can reduce noise and outlier sensitivity. However, the selection of cluster centers is still random and has an effect on the results of clustering, so it is necessary to improve the k-medoids algorithm so that the resulting cluster value can be optimal. Particle Swarm Optimization is an optimization algorithm that is often used and has been proven to improve the results of a clustering. In this case, optimization using Particle Swarm Optimization (PSO) in the selection of the initial cluster center needs to be applied to the k-medoids algorithm so that the results of the cluster can be optimal. The results of the study showed the Davies-Bouldin Index (DBI) value for K-Medoids K 2 = 0.379, K 3 = 0.283, and K 4 = 0.593, while the DBI value PSO + K-Medoids K 2 = 0.088, K 3 = 0.226, and K4 = 0.363. The DBI value shows that PSO optimization on K-Medoids to determine the initial centroid is proven to improve the results of clustering than standard K-Medoids.
Software security: Threats, solutions and challenges

Software security is of great concern as computers have entered almost all walks of life and people at large have become dependent on technology for not only entertainment and communication but for performing tasks involving money and a lot of stake. Software security not only involves securing the software but also user data and communication media. This paper states the several types of security threats that exist since the time networking has evolved, namely, malware, Trojans, viruses, denial of service attacks, and many more. This paper reviews several measures to address these threats. It includes logging, anti-malware, network security methods, and encryption methods. It has been identified that a lot of work has been done to deal with security threats, and it is not only limited to the protection of software but also extends to the protection of data and networks. The existing methods make extensive use of artificial intelligence, and it is identified that there is a need to develop a model that is able to identify known as well as unknown threats. There is a huge scope for research in this area.
An Analytical Inquiry into the Impact of International Trade on Poverty Reduction in Nigeria

This work is propelled by the alarming rate at which poverty has continued to increase, thereby aiding socio-economic challenges in Nigeria. The work through the application of the ARDL and ECM framework of data analysis on secondary data sourced from the Central Bank of Nigeria, calibrated the impact of international trade on poverty reduction in Nigeria from 1981-2021. The result of the study has proven that the relationship between the nation's non-oil export and poverty is negative. However, evidence from the study more so alludes that the country's export of crude oil overtime has not had any meaningful effect of reducing poverty in Nigeria, rather the rate of poverty has continued to rise with the increase in oil exports. Empirical evidence from the study further revealed that Nigeria's imports within the scope of the work has had a significant negative relationship with poverty, which negates the theoretical condition of their expected relationship. Hence, to overturn this positive trend between oil exports and poverty rate, the Nigerian government needs to train and equip the unskilled poor Nigerian with requisite skills and knowledge that would make them gainfully employed in the oil industry, more so the country is admonished to restrict its imports to just commodities that cannot be produced locally.