Reviewerly/exHarmony huggingface dataset:
Investigation related to database design for letter of credit

The "Letter of Credit" is a fundamental payment mechanism in global trade, and its efficiency has significantly declined due to the impact of the COVID-19 pandemic. This document delves deeply into the development and execution of a comprehensive database for managing Letters of Credit (L/Cs). It is designed with a real-world case study derived from the International Settlement Department of the Bank of China. The central goal of this database is to enhance the efficient management and simplification of the complex procedures inherent in L/C transactions. The development process follows a systematic approach, commencing with the establishment of three fundamental assumptions to delineate the scope of the study. Subsequently, the database design progresses through stages involving Entity-Relationship Diagram (ERD) development, relational model construction, and normalization procedures. These steps collectively culminate in the creation of an intelligently structured database comprising thirteen distinct tables. In conclusion, this paper not only delves into the design and implementation of the L/C database but also showcases its practicality through real-world scenarios. The database, fueled by meticulous design and thoughtful implementation, is poised to revolutionize the management of L/C operations, offering enhanced efficiency and accuracy in international trade transactions.
Suspicious Activity Tracking

Abstract: Our offering includes a deep neural network model that can identify firearms in photographs and a machine learning and computer vision pipeline that can detect abandoned luggage in order to identify potential gun-based crime and circumstances involving abandoned luggage in surveillance film. Unusual behavior the technique of identifying undesired human activity in locations and circumstances is called detection. To do this, footage is converted into frames, and the processed frames are then used to analyze the people's sports. YOLOv3 is used to find a niche, in dubious sports like lock breaking and bag snatching, among others. Our gadget has a superb processing pace in addition to appropriate accuracy of detection. It is harder for computers to detect things in videos compared to images because of issues like blurriness or things getting blocked. They propose a solution called Shot Video Object Detector, which is a faster kind of detector for videos. It works by combining information from nearby frames to make better guesses about where objects are. Unlike other methods, Shot Video Object Detector does this by figuring out how things move between frames and then using that info to combine features. It also creates new features by borrowing information directly from neighboring frames using a special structure. Automated surveillance in public areas plays a crucial role in upholding law and order and proactively identifying potential risks to the public. Not only does the procedure automatically identify and detect known crooks, but it also tracks people's and things' movements and uses machine learning algorithms to alert the authorities to any questionable activity
Didactic Use of the Publisher Processor to Enhance Meaningful Learning in Peruvian Secondary School Students

The coronavirus pandemic has changed the course of education from face-to-face to remote. In response to this context, we investigated the positive effect of the didactic use of the Publisher processor to enhance meaningful learning in the competence "explain the physical world based on scientific knowledge about matter and energy, biodiversity, earth and universe" of the science and technology area in fifth grade students of secondary education of a Public Educational Institution of Chiclayo (Peru). For this purpose, a pre-experimental design with pre- and post-test was used with a sample of 102 students selected in a non-probabilistic way. A program based on activities incorporated in learning experiences proposed by the Ministry of Education (Peru) was applied. As a result, 72% of the students increased their academic performance after the application of the program, improving up to five (5) points compared to the initial test. Thus, the development of didactic material using the Publisher processor strengthens learning, which highlights the importance of information technologies in the achievement of active learning and the need to use them in the processes of mediation, reinforcement and teacher feedback in virtual and/or face-to-face scenarios.
Research on the intersection of natural language processing and deep learning

In the past ten years, Natural Language Processing (NLP) has made many surprising progress due to the rapid development of Deep Learning(DL) and further explored the possibility of future development. This article briefly introduces the NLP field, the basic structure of DL, and the impact of the combination of DL and NLP on the NLP field. Finally, it reviews the limitations of the two under the constraints of current science and technology and looks forward to the possibility and direction of their future development. Appropriately applying DL to NLP can indeed bring great progress to the core areas and applications of NLP. Still, at the same time, the development of NLP will also be limited by the shortcomings of DL. It is necessary to continuously optimize the DL model while taking advantage of the advantages of DL. The most critical factor to promote the development of DL and NLP in the future.
Intelligent Navigation System based on Big Data Traffic System

This paper studies the navigation technology of the On-Orbit Servicing Spacecraft (OSS) and proposes an overall solution for the OSS navigation system. The composition of the OSS satellite navigation simulation system and simulation supporting environment are studied. Then a new SSUKF navigation filter is proposed based on the hyper spherical distributed feature sampling point transform algorithm (SSUT) and the non-scanning Kalman filter (UKF). The numerical simulation of the OSS system is studied based on MATLAB/RTW. Finally, the SSUKF algorithm and the conventional UKF algorithm are simulated digitally. The effectiveness and advanced nature of the hybrid Kalman filter applied to spacecraft autonomous navigation are verified.
Human Pose Estimation Algorithm Using Optimized Symmetric Spatial Transformation Network

Human posture estimation is a crucial topic in the computer vision field and has become a hotspot for research in many human behaviors related work. Human pose estimation can be understood as the human key point recognition and connection problem. The paper presents an optimized symmetric spatial transformation network designed to connect with single-person pose estimation network to propose high-quality human target frames from inaccurate human bounding boxes, and introduces parametric pose non-maximal suppression to eliminate redundant pose estimation, and applies an elimination rule to eliminate similar pose to obtain unique human pose estimation results. The exploratory outcomes demonstrate the way that the proposed technique can precisely recognize the human central issues, really work on the exactness of human posture assessment, and can adjust to the intricate scenes with thick individuals and impediment. Finally, the difficulties and possible future trends are described, and the development of the field is presented.
Improving Big Data Recommendation System Performance using NLP techniques with multi attributes

Due to the wide availability of big data, institutions and companies are currently concentrating on developing highly effective recommender systems for their users. Traditional recommender systems use standard information like user, item, and ratings. However, this data may not be sufficient for precise results. To enhance accuracy, it is recommended to include additional information such as textual data in the recommendation system. When dealing with large textual data, employing Natural Language Processing (NLP) techniques is essential for effective data analysis. Hence, this paper proposed a novel big data recommender system that enhances collaborative filtering (CF) results by leveraging NLP techniques and dealing with multiple attributes. The study constructs two big data recommendation system models by using a machine learning algorithm. In both models, the Alternating Least Squares (ALS) algorithm within the Apache Spark big data tool was utilized. The first model did not incorporate NLP techniques, while the second model considered the novel NLP techniques by taking into account the user's review comments. A dataset of more than 3 million ratings and reviews was gathered from the Amazon website, amounting to a size of 3.1 GB. The results demonstrated significant improvement after incorporating the suggested NLP-based techniques with multiple attributes.
Metadata design for the first electronic learner corpus of Romanian

This paper introduces an ongoing work of collecting, annotating and documenting the first digital Romanian Learner Corpus (LECOR), focusing on its metadata. We shortly describe the institutional context of the project, the current state of the art in the field, the objectives in terms of structure, dimensions and annotations and what work has already been done at this stage of the project. Then we present the modular structure of the metadata scheme and a detailed account of all the metadata fields and their possible values, from general metadata concerning the whole corpus (Section 3.1), to metadata organised around the student/learner (Section 3.2) and text/composition (Section 3.3). We will also give some examples of how metadata has been dealt with in various researches (including based on LECOR corpus).
Open Information and Exceptions Policy of the Natural History Museum, London

There have been few, if any, open data and information management policies openly published from natural science collections. This paper contextualises the rationale for publishing the Open Information and Exceptions Policy of the Natural History Museum, London and provides the policy itself. The policy outlines how the Natural History Museum puts the principle of 'open by default' into practice; and includes sections on purpose and scope, relationship to relevant legislation (which always takes precedence over the policy), the categories of possible exceptions to open information release, what happens when exceptions are declared, relations to UK government information security classifications and definition of terms.
Application of Discrete and Fast Fourier Transforms to Increase the Speed of Multiscale Image Analysis

The paper compares the accuracy of reconstruction using different wavelets, and the authors also use fast and discrete Fourier transforms together to calculate the forward and inverse continuous wavelet transform in the frequency domain. Due to the use of calculations in the frequency domain, it becomes possible to perform decomposition, reconstruction, image filtering, and other transformations with high performance and precision. For multiscale signal analysis, a wavelet with a rectangular amplitude-frequency response has been constructed, which allows for an increase in the accuracy of decomposition and reconstruction compared to the Mallat algorithm presented in Matlab computer mathematics. At the same time, the time of multiscale analysis is reduced several times compared to the Mallat algorithm.
Correction: Gerber, M.; Pillay, N. Automated Design of the Deep Neural Network Pipeline. Appl. Sci. 2022, 12, 12215

The authors would like to make the following corrections to this published paper [...]
FACTORS EFFECTING JOB SATISFACTION FOR ONLINE EDUCATION AMONG LECTURERS IN BANGLADESH

The global pandemic caused by Covid-19 infectious disease during the year 2020 has pierced the life of humankind in almost all countries and all members of the society. This pandemic issue limits one to cope with a changing work environment and it induces both structural and functional consequences on the education system. Thus, the online education system emerged and repositioned itself as one of the most effective mediums of education system recently. This study examined decision factors such as happiness, quality of work-life commitment and attitude toward online education and its influence on job satisfaction of academic staffs in Bangladesh. A quantitative research approach has been taken using a random sampling method to collect data from 293 lecturers working in colleges and universities in the metropolitan city of Dhaka. For the study, Exploratory Factor Analysis and Structural Equation Modelling techniques were performed. The findings helped in filling the satisfaction-behavioural gap in understanding attitude measures of academic staffs toward conducting courses in online mode.
Factors and Conditions of Practical Training for Future Choreographer-Teachers

The article addresses the task emphasized in the Federal State Educational Standards for Higher Education in all areas of choreographer training, as well as in the professional standard “Teacher of Additional Education for Children and Adults” (Order of the Ministry of Labor and Social Protection of the Russian Federation, October 22, 2021), namely, the preparation of future specialists in choreography for teaching activities. The goal of the article is to draw attention to the factors that determine this process since pedagogical tasks are present in all types of professional activities for future choreographers. The study was conducted based on comparative, critical analysis, synthesis, generalization, and interpretation methods. Examination of regulatory documents, psychological-pedagogical scientific literature, and practice allowed us to identify factors that have the greatest influence on the practical training of future choreographers and categorize them based on their sources of impact as external and internal. External factors include: cultural-educational environment, interaction with teachers, and student community. Internal factors include: passion for the profession, creativity, self-development, self-education, and self-discipline. Based on these factors, conditions were identified and justified that can significantly improve this process. Such conditions include: enriching the pedagogical component in teaching special disciplines to choreography students; involving students in practice-oriented activities during internships; and using new technologies in teaching disciplines. In conclusion, it is stated that practical training for teaching activities of choreography students represents a process of complex factors influenced by the specifics of choreographic education and implemented through the theoretically substantiated pedagogical conditions discussed in the article.
Optimizing Performance of Cloud Infrastructure Through Effective Resource Scheduling

Cloud computing has emerged as a very promising technology that has garnered significant interest from both industry professionals and academic researchers. Cloud computing service models refer to the various types of services that are provided, including hardware and software infrastructure, platforms for application development, testing, and deployment, as well as enterprise software that is readily available for usage through subscription. Public cloud computing involves the delegation of IT infrastructure, storage, or applications to an external service provider. The presence of a cloud infrastructure also signifies the existence of geographically distributed computing resources. The utilisation of resources in conjunction with cloud computing is not exclusive to large-scale organisations, as it may be employed by entities of any size. Numerous services are offered based on a fee-for-use model, rendering them cost-effective for organisations of various sizes. Cloud service providers are obligated to provide consumers with cloud services on demand, as there is a growing need for such services. This requirement stems from the necessity to decrease the size of large data volumes, which in turn leads to cost savings in maintaining extensive storage systems. The overall effectiveness of cloud computing environments is directly related to the operational performance of cloud infrastructure. This phenomenon holds substantial significance in the realm of optimization, since it enhances the overall efficiency of the underlying cloud architecture. The proposed technique exhibits a significant effectiveness in enhancing cloud performance, as it manifests improvements for both service providers and cloud customers.&#x0D; Key Words: Cloud Computing, Task Scheduling, Cloud Infrastructure, Resource Scheduling, Performance Analysis and Infrastructure as Service.
Artificial Intelligence as a Harbinger of Significant Changes in Education

The rapid development of programs based on the principles of machine learning (ML) and artificial intelligence (AI) signals significant changes in the components of education, namely in the provider, the tool of transmission, and the recipient of knowledge. Historical data analysis regarding the key functions of education serves as the basis for identifying fundamental innovations introduced through AI and ML. The impact of writing, printing, and the Internet has significantly altered the tool for knowledge transmission, influencing the volume of information and the number of knowledge recipients. The implementation of AI and ML transforms not only the tool but also the provider of knowledge itself, which can become impersonal thanks to the corresponding computer programs. With the historically justified increase in the volume of knowledge possessed by humanity, there is a transformation observed in education systems. This is especially true for democratic societies, where the emphasis is increasingly shifting from providing a large amount of knowledge to developing critical thinking. It has been researched that programs based on AI and ML, applying linguistic models, are capable of effectively systematizing knowledge. This lays the foundation for personalizing the entire education process for a specific knowledge recipient, without burdening the provider. However, there have been cases when such imitation misleads scientists, who perceive it as attempts at communication between programs that have an equivalence to the human level. The conclusions drawn indicate a significant transformation of the education system caused by AI and ML-based programs. However, intelligent programs are unable to evolve into independent knowledge recipients due to their inability to consciously attribute meaning to information, transforming it into knowledge.
Development of A Curriculum Type Preference Scale for Preschool Education Vocational University

Exploring students’ preferences for curriculum types is beneficial for further designing curriculum structures based on the differentiated needs of students, thereby improving curriculum and education quality. To make up for the lack of an effective measurement instrument for investigating the curriculum type preferences of Chinese vocational university students majoring in preschool education, a 5-point Likert scale is designed and developed in this study. The development procedure consists of three phases: (1) the scale generation phase; (2) the scale evaluation phase, including expert evaluation and target population pretesting; and (3) the scale testing phase, including first testing, second testing, and third testing. The results show that the initial scale consisted of three constructs (theory curriculum, application curriculum, and internship curriculum) and 21 items in total. By removing one item from the initial scale, 20 items in the final scale have been proven to have acceptable validity and reliability. The conclusions suggest that the curriculum type preference scale can be applied in practice, and the scale development procedure and methods in this study can serve as a theoretical reference for scale developers.
QUANTUM COMPUTING IN BIG DATA ANALYTICS: A COMPREHENSIVE REVIEW: ASSESSING THE ADVANCEMENTS, CHALLENGES, AND POTENTIAL IMPLICATIONS OF QUANTUM APPROACHES IN HANDLING MASSIVE DATA SETS

This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics.&#x0D; Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.
A GCC Artificial Food Additives Management based Mobile Application Development

Artificial food additives pose significant health risks to Gulf Cooperation Council (GCC) citizens despite regional authorities' extensive medical, legislative, and technological efforts. Literature highlights the detrimental impacts of these additives, including malnutrition, digestive disorders, respiratory problems, skin issues, hives, nausea, diarrhea, shortness of breath, allergic reactions, high blood pressure, and tumors. The research project at hand aims at becoming the first official and comprehensive mobile application of its own in the GCC region that manages the calculation and demonstration of an up-to-date health and legal knowledge base of the impacts of artificial additives, enhances the awareness, automatically recognizes the artificial additives, and provides alternative solutions, for both android and IOS mobile platforms. This research project introduces "Weqaya," a pioneering mobile application designed to manage, educate, and raise awareness about the effects of artificial additives. Weqaya provides real-time health and legal information, identifies additives, and suggests alternative solutions for Android and iOS platforms. The project employs an Agile-based SDLC model to explore, develop, and evaluate the food additive phenomena in Weqaya. The application's usability evaluation scores a promising 95.21%, indicating its potential utility for GCC health ministries, dietitians, academics, researchers, and food producers in enhancing knowledge and promoting non-artificial food options.
Principle of explicability: regulatory challenges on artificial intelligence

This essay aims to examine topic related to artificial intelligence, explaining how it works and specially studying aspects of regulatory framework. The paper approaches the principle of explicability of AI decisions, performing an analysis on the braziilian parliamentary discussion and brazilian data protection act. The hermeneutic method was adopted as well as literature review.