Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Decentralized Student Verification Platform

Each year, billions of students complete their studies, continue their education, and either find employment in the private sector or in graduate school. The business also invests a lot of effort in verifying the validity of the applicant’s certificate. To reflect their credentials and degrees, people frequently counterfeit certificates. An expert con artist’s fake certificate is never simple to identify. Therefore, improving the certification verification process is really necessary. Recently, Blockchain technology was introduced, which might replace the manual student verification method. This work provides a decentralized student verification infrastructure built on the Blockchain that will make it simple to issue, review, and validate academic credentials. Based on the comparison of hashes currently present in the Blockchain, the student’s identity and academic history are both confirmed. The archives are also linked to the understudy in the suggested method to provide an additional layer of assurance. This approach will aid academics and institutions in keeping the system transparent and secure.
Accounting Students’ Perceptions on E-Learning During Covid-19 Pandemic: Case Study of Accounting and Financial Students in Gulf University—Bahrain

The study aims to know the perception of accounting and financial students on gulf university about e-learning during the covid-19 pandemic and to know the advantages of e-learning during the covid-19 pandemic according to the perception of gulf university students. The study used an online survey which distributed to all the students who used online accounting e-learning during covid-19, 83 respondents were collected from them. The study found that, the students agreed that the online e-learning was effective and has many advantages to them such as simple to use and they can to the material of the lecture anytime, the contact with the university lectures has improved, improved their communication with other students and the university succeeded by facilitate the accounting and financial courses with e-learning resources. Future studies can examine the perception of the accounting students in other Bahraini universities as well as in developing countries about the using of e-learning in teaching accounting and finance courses, and examine the links between e-learning accounting education and sustainability development goal No. 4 quality education.
Evaluating sparse aperture imaging performance: simulation and experiment

Sparse apertures can greatly improve the size, weight, power and cost (SWaP-C) in comparison to larger multi-meter diameter monolithic apertures for Intelligence, Surveillance and Reconnaissance (ISR) applications. However, their system design is more complex and requires careful optimization of the aperture configuration. The Air Force Research Lab (AFRL) has developed a simulation toolkit that allows for rapid prototyping of sparse aperture designs to evaluate their optical performance that assists in concept design development. The toolkit is based upon a novel formalism that combines imaging and interferometry principles within the quasi-monochromatic approximation to build a complex synthetic pupil function containing all the sub-pupil phase information. Defining the sub-pupil phase information is customizable with user-defined low order Zernike’s (i.e. piston and tilt) or by importing higher order Zernike’s from commercial interferometric optical test results. This toolkit has been validated using a two-aperture sparse aperture testbed that is constructed completely from commercial off-the-shelf (COTS) components and is reconfigurable. In addition we have evaluated the performance of a six-aperture interferometric imaging array developed by HartSCI. With these recent advances in testing and modeling capabilities, AFRL hopes to investigate opportunities for sparse aperture technologies that integrate into the modern ISR domain.
Certificate Authentication System Using Blockchain

An individual’s qualification is determined by the educational certificates, which serve as proof. Nowadays each and everything has been represented in digital form in which the academic certificate of school and college education is digitalized by the educational board or University and granted to the students. It had been troublesome to keep up the academic qualification records and can be a loophole to fraud and unethical. The process of verification and evaluation was a tiresome and unwieldy process for both the employer organization and academic institution. The traditional method of approving the lawfulness and originality of certificates had not given abundant potency in averting forgery. In our project, a trustable and spoil-proof blockchain-based certificate authentication methodology is proposed to make the employee verification process easier for any employer. It verifies the essential documents required by the applicant for an interview process. Unlike the conventional verification system, the proposed methodology is maintained by blockchain-based sealed smart contracts to automatize the authentication method and supply lucidity and authenticity needed for developing a quick, reliable, and cost-efficient authentication system. The hash value of the digital certificate is obtained by employing the SHA3-512 algorithm which enhances security. Then the academic records are represented in the blockchain system in the Merkle tree structure and these certificates are validated by using the smart contracts.
Cyclic Action Graphs for goal recognition problems with inaccurately initialised fluents

Abstract Goal recognisers attempt to infer an agent’s intentions from a sequence of observed actions. This is an important component of intelligent systems that aim to assist or thwart actors; however, there are many challenges to overcome. For example, the initial state of the environment could be partially unknown, and agents can act suboptimally and observations could be missing. Approaches that adapt classical planning techniques to goal recognition have previously been proposed, but, generally, they assume the initial world state is accurately defined. In this paper, a state is inaccurate if any fluent’s value is unknown or incorrect. Our aim is to develop a goal recognition approach that is as accurate as the current state-of-the-art algorithms and whose accuracy does not deteriorate when the initial state is inaccurately defined. To cope with this complication, we propose solving goal recognition problems by means of an Action Graph. An Action Graph models the dependencies, i.e. order constraints, between all actions rather than just actions within a plan. Leaf nodes correspond to actions and are connected to their dependencies via operator nodes. After generating an Action Graph, the graph’s nodes are labelled with their distance from each hypothesis goal. This distance is based on the number and type of nodes traversed to reach the node in question from an action node that results in the goal state being reached. For each observation, the goal probabilities are then updated based on either the distance the observed action’s node is from each goal or the change in distance. Our experimental results, for 15 different domains, demonstrate that our approach is robust to inaccuracies within the defined initial state.
ON RELATIONSHIPS BETWEEN DIFFERENT CONCEPTS OF HYPOELLIPTICITY

The paper investigates connections and relations between different types (concepts) of hypoelliptic differential operators (symbols (characteristic polynomials) of these operators).
A knowledge graph of interlinking digital records: the case of the 1997 Korean financial crisis

Purpose Despite ongoing research into archival metadata standards, digital archives are unable to effectively represent records in their appropriate contexts. This study aims to propose a knowledge graph that depicts the diverse relationships between heterogeneous digital archive entities. Design/methodology/approach This study introduces and describes a method for applying knowledge graphs to digital archives in a step-by-step manner. It examines archival metadata standards, such as Records in Context Ontology (RiC-O), for characterising digital records; explains the process of data refinement, enrichment and reconciliation with examples; and demonstrates the use of knowledge graphs constructed using semantic queries. Findings This study introduced the 97imf.kr archive as a knowledge graph, enabling meaningful exploration of relationships within the archive’s records. This approach facilitated comprehensive record descriptions about different record entities. Applying archival ontologies with general-purpose vocabularies to digital records was advised to enhance metadata coherence and semantic search. Originality/value Most digital archives serviced in Korea are limited in the proper use of archival metadata standards. The contribution of this study is to propose a practical application of knowledge graph technology for linking and exploring digital records. This study details the process of collecting raw data on archives, data preprocessing and data enrichment, and demonstrates how to build a knowledge graph connected to external data. In particular, the knowledge graph of RiC-O vocabulary, Wikidata and Schema.org vocabulary and the semantic query using it can be applied to supplement keyword search in conventional digital archives.
Virtual reality environment in pharmacy education: A cyclical study on instructional design principles

Abstract Background It is crucial to use innovative tools to gain clinical reasoning skills through experiential learning in pharmacy education, and one of the most effective tools used in this is virtual reality. However, the lack of research that empirically demonstrates the instructional principles of virtual environment design, especially in the clinical setting, has resulted in a significant gap in the current literature. Aim In this study, it was aimed to determine the instructional design principles of a virtual environment for the clinical pharmacy. Method The study was conducted as a design‐based research. The data of the study related to three components of the developed virtual reality environment in pharmacy education (VRPE). These components, scenario development interface‐scenario development interface, virtual environment, and scenario, were collected through interviews, observation, document review, and a system usability scale. Instructional design principles for the VRPE were determined through analysis of the collected data. Results The prominent instructional design principles of the VRPE were revealed as immersion, interaction, diversity, flow, aesthetics‐ergonomic‐universal design, informing, and retest availability. The significance of the study was the adoption of a holistic perspective in the design of a virtual reality environment for pharmacy education. In addition, one of the most critical features of the study was eliminating the expertise barrier in virtual environment design. Thus, it was concluded that the VRPE is an extraordinary tool that can be used for pharmacy education to promote experiential learning.
Enhancing concrete structures education: Impact of virtual reality on motivation, performance and usability for undergraduate engineering students

Abstract Background Study The research discusses the need for a paradigm shift in engineering education current practices to accommodate the digital native students. The paper emphasizes the importance of integrating disruptive technologies, namely Virtual Reality (VR) through Head Mounted Displays VR (HMD VR) and Desktop Based VR (DB VR) and comparing it with the traditional teaching methods. The research focuses on second‐year undergraduate students enrolled in a concrete structures course. Objectives The objective of the study is to analyse the impact of DB VR and HMD VR on students' motivation, performance and workload in comparison with traditional 2D drawings. The study also aims to investigate how simulation sickness and system usability could impact students' motivation and performance when interpreting concrete structures. Methods The study divided second‐year civil engineering students into three groups, 2D, DB VR and HMD VR. The performance and motivation levels of students were assessed based on the number of mistakes identified and an intrinsic motivation survey. Furthermore, data on system usability and simulation sickness were collected. Spearman rank correlation was conducted to analyse the relationships between the variables tested. Results The results have shown that students' motivation and performance were much higher when using VR in general and HMD VR specifically, students' efficiency and accuracy were also observed. On the other hand, usability was found to be moderately correlated with motivation, with students rating HMD VR as the most usable approach. A moderate inverse correlation was observed between simulation sickness and motivation, indicating that higher simulation sickness led to lower motivation levels. Conclusion The study concludes that integrating VR in general, DB VR, or HMD VR into a concrete structures course for undergraduate students can significantly improve students' performance and motivation. Furthermore, enhancing usability of any of the methods used can also improve the performance and motivation. On the other hand, simulation sickness inversely affects motivation and performance, therefore, VR developers and educators should not neglect it.
A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems.
Leveraging High-Resolution Long-Wave Infrared Hyperspectral Laboratory Imaging Data for Mineral Identification Using Machine Learning Methods

Laboratory-based hyperspectral imaging (HSI) is an optical non-destructive technology used to extract mineralogical information from bedrock drill cores. In the present study, drill core scanning in the long-wave infrared (LWIR; 8000–12,000 nm) wavelength region was used to map the dominant minerals in HSI pixels. Machine learning classification algorithms, including random forest (RF) and support vector machine, have previously been applied to the mineral characterization of drill core hyperspectral data. The objectives of this study are to expand semi-automated mineral mapping by investigating the mapping accuracy, generalization potential, and classification ability of cutting-edge methods, such as various ensemble machine learning algorithms and deep learning semantic segmentation. In the present study, the mapping of quartz, talc, chlorite, and mixtures thereof in HSI data was performed using the ENVINet5 algorithm, which is based on the U-net deep learning network and four decision tree ensemble algorithms, including RF, gradient-boosting decision tree (GBDT), light gradient-boosting machine (LightGBM), AdaBoost, and bagging. Prior to training the classification models, endmember selection was employed using the Sequential Maximum Angle Convex Cone endmember extraction method to prepare the samples used in the model training and evaluation of the classification results. The results show that the GBDT and LightGBM classifiers outperformed the other classification models with overall accuracies of 89.43% and 89.22%, respectively. The results of the other classifiers showed overall accuracies of 87.32%, 87.33%, 82.74%, and 78.32% for RF, bagging, ENVINet5, and AdaBoost, respectively. Therefore, the findings of this study confirm that the ensemble machine learning algorithms are efficient tools to analyze drill core HSI data and map dominant minerals. Moreover, the implementation of deep learning methods for mineral mapping from HSI drill core data should be further explored and adjusted.
A Blockchain-Enabled Approach for Enhancing Synchrophasor Measurement in Smart Grid 3.0

Smart Grid 3.0 is the latest evolution of the smart grid and incorporates advanced computing and communication technologies. The synchrophasor communication system plays a critical role in wide-area measurement systems (WAMS) for real-time protection and control of power systems, supporting the objectives of Smart Grid 3.0. This system relies on synchrophasor communication technologies, where Phasor Measurement Units (PMUs) transmit synchrophasor data to Phasor Data Concentrators (PDCs) over the synchrophasor communication network. The communication infrastructure of this network is based on the TCP/IP protocol stack, which, unfortunately, is susceptible to cyberattacks, posing security threats such as data tampering and false data injection. These vulnerabilities undermine the intended benefits of synchrophasor applications in terms of situational awareness, observability, grid reliability, resiliency, and synchronized monitoring and control in the smart grid. To address these challenges, it is crucial to enhance the security, integrity, and confidentiality of synchrophasor data within the communication system. This paper proposes a blockchain-based synchrophasor communication system that preserves the security and integrity of synchrophasor data. In this paper, an architecture is proposed for a synchrophasor communication system based on blockchain technology. The proposed architecture aims to enhance the security and integrity of synchrophasor measurements. Furthermore, the architecture is developed as a peer-to-peer distributed blockchain network, leveraging the robustness of a distributed, decentralized, hierarchical PDC architecture. To evaluate the efficacy of the proposed architecture, two case studies, one using the IEEE 9 bus and the other using IEEE 14 bus systems are considered. Moreover, various challenges with potential solutions are also recommended. The proposed work is envisioned to contribute to the advancement of Smart Grid 3.0 by adopting blockchain technology for synchrophasor applications.
Space Target Tracking with the HRRP Characteristic-Aided Filter via Space-Based Radar

Approaching space target tracking is a typical and challenging mission in the space situational awareness (SSA) field. As the space-based radar is able to monitor the space targets of interest full-weather all-time, the space-based radar system is utilized in this paper. However, most multi-target tracking (MTT) filters in target tracking studies merely utilize the location or narrow measurements, and many potentially valuable electromagnetic scattering characteristics are missed, which leads to space target false tracking problems. The space-based radar transmits a wide-band signal, and the measured high-resolution range profile (HRRP) information is an effective characteristic for different target discrimination. Therefore, the HRRP characteristics of space targets are implemented into the update recursion of the MTT filter, which can be utilized to improve the tracking performance. Then, to predict the target HRRP sequence, the geometrical theory of diffraction (GTD) model is utilized. Additionally, a modified spatial spectrum method with a novel covariance matrix is designed to improve the scattering parameter estimation accuracy. Finally, an adapting threshold is devised for merging the Gaussian mixture (GM) components weights. The proposed threshold is on the basis of the proposed HRRP characteristic-aided probability hypothesis density (PHD) filter, and it can tackle the problem of space target discrimination. Simulation results validate the effectiveness and robustness of the proposed probability hypothesis density (HGI-PHD) filter aided by HRRP information and improved with GM weights.
Classifying Poor Postures of the Neck and Spine in Computer Work by Using Image and Skeleton Analysis

When using a desktop computer, people tend to adopt postures that are detrimental to their bodies, such as text neck and the L-posture of leaning forward with their buttocks out and their shoulders against the backrest of the chair. These two postures cause chronic problems by bending the cervical and thoracic spines and can have detrimental effects on the body. While there have been many studies on text neck posture, there were limited studies on classifying these two postures together, and there are limitations to the accuracy of their classification. To address these limitations, we propose an algorithm for classifying good posture, text neck posture, and L-posture, the latter two of which may negatively affect the body when using a desktop computer. The proposed algorithm utilizes a skeleton algorithm to calculate angles from images of the user’s lateral posture, and then classifies the three postures based on the angle values. If there is sufficient space next to the computer, the method can be implemented anywhere, and classification can be performed at low cost. The experimental results showed a high accuracy rate of 97.06% and an F1-score of 95.23%; the L posture was classified with 100% accuracy.
Analisis Keamanan Sistem Informasi Website SMA Negeri 1 Sokaraja Menggunakan Metode Penetration Testing Execution Standard (PTES)

Technological developments in the world continue to advance along with current developments. It is also in line with the development of the internet today, where users are increasingly increasing. That way, it will be very vulnerable to hacker attacks. An attack that is often used by hackers to break into a database using SQL Injection techniques. SQL Injection is a hacking technique used to enter or infiltrate a website's database system, which aims to find out the contents of the database and the information contained on the site. In this research, researchers used the Penetration Testing Execution Standard (PTES) method to analyze vulnerabilities and also penetrate the SMA Negeri 1 Sokaraja website. The Penetraton Testing Execution Standard (PTES) method has 7 stages. The results of the security analysis of the SMA Negeri 1 Sokaraja website, found 11 vulnerabilities which were successfully scanned using the OWASP ZAP tool. Of the 11 vulnerabilities that were successfully scanned, there was 1 vulnerability that had the highest risk. This vulnerability is found in SQL Injection attacks. In this study, researchers managed to enter the MySQL database system of the SMA Negeri 1 Sokaraja website using the SQL Injection attack technique. In the SMA Negeri 1 Sokaraja website database, researchers managed to find important data such as the SMA Negeri 1 Sokaraja website admin username and password.