Reviewerly/exHarmony huggingface dataset:
Intelligent Navigation System based on Big Data Traffic System

Authors:
raw_author_name: Xiu Zhang
raw_author_name: Jian Kang
raw_author_name: Haicun Yu

Abstract: 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

Authors:
raw_author_name: Nor Azizah Ali
raw_affiliation_string: Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor 81310, Malaysia.
raw_author_name: Azlan bin Mohd Zain
raw_affiliation_string: Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor 81310, Malaysia.
raw_author_name: Muhalim Mohamed Amin Amin
raw_affiliation_string: Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Johor 81310, Malaysia.

Abstract: 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

Authors:
raw_author_name: Hoger K. Omar
raw_author_name: Mondher Frikha
raw_author_name: Alaa Khalil Jumaa

Abstract: 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.
Open Information and Exceptions Policy of the Natural History Museum, London

Authors:
raw_author_name: Matt Woodburn
raw_author_name: Laurence Livermore
raw_author_name: Ben Scott
raw_author_name: Vincent Smith
raw_author_name: Helen Hardy

Abstract: 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

Authors:
raw_author_name: Viliam Ďuriš
raw_affiliation_string: Department of Mathematics, Constantine the Philosopher University, Tr. A. Hlinku 1, Nitra, 949 01, Slovakia
raw_author_name: Vladimir I. Semenov
raw_affiliation_string: Department of General Physics, I.N. Ulyanov Chuvash State University, Cheboksary, 428015, Russia
raw_author_name: Sergey G. Chumarov
raw_affiliation_string: PDepartment of Radio Engeneering, I.N. Ulyanov Chuvash State University, Cheboksary, 428015, Russia

Abstract: 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

Authors:
raw_author_name: Mia Gerber
raw_affiliation_string: Department of Computer Science, University of Pretoria, Hatfield, Pretoria 0028, South Africa
raw_author_name: Nelishia Pillay
raw_affiliation_string: Department of Computer Science, University of Pretoria, Hatfield, Pretoria 0028, South Africa

Abstract: The authors would like to make the following corrections to this published paper [...]
Factors and Conditions of Practical Training for Future Choreographer-Teachers

Authors:
raw_author_name: Anastasia A. Davydova

Abstract: 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

Authors:
raw_author_name: None M.Abdullah
raw_author_name: None Dr. M. Mohamed Surputheen

Abstract: 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.
Development of A Curriculum Type Preference Scale for Preschool Education Vocational University

Authors:
raw_author_name: Xinyan Zhu

Abstract: 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

Authors:
raw_author_name: None Andrew Ifesinachi Daraojimba
raw_affiliation_string: Department of Information Management, Ahmadu Bello University, Zaria, Nigeria
raw_author_name: None Samuel Onimisi Dawodu
raw_affiliation_string: NDIC, Nigeria

Abstract: 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

Authors:
raw_author_name: Ehab Juma Adwan
raw_affiliation_string: University of Bahrain

Abstract: 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

Authors:
raw_author_name: Alexandre de Souza Araújo
raw_affiliation_string: SENAI/CIMATEC

Abstract: 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.
PENGEMBANGAN SUMBER BELAJAR BERBASIS WEB PADA MATA PELAJARAN PAI DI SMA NEGERI 1 TENJOLAYA BOGOR

Authors:
raw_author_name: Zaenal Abidin
raw_affiliation_string: STIT SIROJUL FALAH

Abstract: This research is based on the development of technology and information that has developed greatly towards the world of education. Utilization of the web as a learning medium in accordance with the learning styles of students who are heading towards the digital era, web-based learning is a learning activity that utilizes website media that can be accessed through the internet network. internet network access provided by the school then the web-based learning media can be used by the teacher as one of the learning media where the teacher still uses learning in every learning. This research focuses on the problem of How to Develop WEB-Based Learning Media in Islamic Religious Education Subjects at SMAN 1 Tenjolaya Bogor.This research studies: 1) Producing web-based learning media on Islamic Religious Education subjects; 2) Describe the results of student responses to the use of web-based learning media in Islamic Religious Education subjects&#x0D; Related to this research is research and development (Research &amp; Development) or R&amp;D related to products, instructional media development, using ADDIE models which include Analysis, Design, Development, Implementation, and Evaluation. Research data obtained through observation, questionnaire, and documentation. Furthermore, data analysis using the results score from the validation of media experts, material experts and student responses to web-based learning media.&#x0D; The results of the research show: 1) the development by the researcher carries out student character analysis, media analysis, and data analysis of media and material experts, then designs the media with a web site application that contains a blog, media development on materials, desired products, validation, tests try to do 28 students of class X, implement the developed media then given to the teacher to be used for students then the researcher gives a questionnaire to students about perceptions of the developed media, then a further evaluation of the media based on the assessment of students and the media developed. 2) Inviting students about Learning Media by providing a questionnaire of 28 students using web-based learning media that are close to God, the media developed "feasible" is used as a learning medium in class X SMA1 Tenjolaya Bogor
Performance Analysis and Comparison of Machine Learning Algorithms for Breast Cancer Dataset

Authors:
raw_author_name: Nagadevi Darapureddy
raw_affiliation_string: Department of ECE, Chaitanya Bharathi Institute of Technology, Hyderabad, A.P, India.

Abstract: Breast cancer is one of the most commonly found disease in women. In this research Wisconsin breast cancer data is considered. Data preprocessing and feature selection is done on this dataset. Later machine learning algorithms were applied on this dataset for classification of the disease. In machine learning, Classification is one of the most important research area. Classification allocates the given input to a known category. In this paper different machine algorithms like Logistic regression (LR), Decision tree (DT), Support vector machine (SVM), K nearest neighbors (KNN) were implemented on this dataset. The models were trained and tested with k-fold cross validation data. Accuracy and run time execution of each classifier are implemented in python. From the results it can be observed that Decision Tree is giving an accuracy of 90.7%.
Simulation Stock Investment System Based on Vue and Python

Authors:
raw_author_name: Jiayi Cui
raw_affiliation_string: Computer Science School, University of New South Wales,2052 NSW, Australia

Abstract: Economic Management and Big Data Application, pp. 892-900 (2024) No AccessSimulation Stock Investment System Based on Vue and PythonJiayi CuiJiayi CuiComputer Science School, University of New South Wales,2052 NSW, Australiahttps://doi.org/10.1142/9789811270277_0079Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Stocks have become a common way to manage money and invest. However, it is too risky for initial users to invest directly. In the era of artificial intelligence, computer technology can be combined with many fields. This paper summarizes and designs a stock investment simulation system which combines the front and back end. The front end uses the VUE framework, and the back end builds a database. The back end uses Python to write the server to help novices quickly get familiar with the form of stock investment methods. This system also adds a risk assessment method to evaluate the risk coefficient of the stock, so that beginners can better adapt to real stock investment. Keywords: Stock investment simulatorArtificial intelligenceRisk predictionEuclidean DistanceVuepython FiguresReferencesRelatedDetails Recommended Economic Management and Big Data ApplicationMetrics History KeywordsStock investment simulatorArtificial intelligenceRisk predictionEuclidean DistanceVuepythonPDF download
Pemanfaatan Media YouTube dan Blog Pembelajaran Materi Statistika dan Hubungannya dengan Kemampuan Pemecahan Masalah Matematis dan Prestasi Belajar

Authors:
raw_author_name: Samuel Lukas
raw_affiliation_string: Universitas Pelita Harapan Jakarta

Abstract: This research aims to examine the use of learning media that is good, effective and appropriate to the needs and characteristics of students through YouTube media and Learning Blogs and its relationship with mathematical problemsolving abilities and learning achievement. The method used was quantitative quasi-experiment with correlation tests on mathematical problem solving abilities and learning achievement for classes VIII C and VIII D at SMP Tarakanita 2 Jakarta with 58 respondents. The Cronbach's Alpha test on the YouTube media test instruments (0.83), Blog (0.82), Problem Solving (Pretest 0.92 and Posttest 0.86), and Achievement (0.90) shows high validity and reliability. Based on non-parametric hypothesis testing using the Spearman Test method, it is known that there is a correlation between the use of YouTube media and Problem Solving (R-count 0.37), YouTube and Achievement (R-count 0.41), Blogs and Problem Solving (R-count 0. .42), and Blog with Achievement (R-count .39). The non-parametric Mann Whitney test in the control class and experimental class regarding achievement was stated to be identical with the same mean and variance so that there was no difference in the increase in NGain in the two classes.