Reviewerly/exHarmony huggingface dataset:
Comparison of Face Recognition Using PCLDA and Neural Network

Facial recognition is a complex multidimensional structure that demands sophisticated computing techniques for authentication purpose. In this paper, we introduce the Integral Normalized Gradient Image (INGI) algorithm with various normalizing stages. The system comprises a novel illumination insensitive preprocessing method, a hybrid Fourier based feature extraction and matching process. The Pre-processing method is grounded in the analysis of the facial imaging model, considering intrinsic and extrinsic factors of the human face. Feature extraction encompasses hybrid Fourier features extracted from different frequency bands and multiple face models. By deriving Fourier features from three Fourier domains and three distinct frequency bandwidths, we acquired additional complementary features. These features are individually classified using Principal Component and Linear Discriminant Analysis (PCLDA). This approach enables in analyzing a face image from the various viewpoints for identity recognition. Furthermore, we propose multiple face models based on different eye positions with a same image size. This contributes significantly to enhancing the performance of the proposed system. Recognition is achieved through Euclidean Distance and Neural Network based classifier, resulting in a recognition accuracy of approximately 89.23% for the Euclidean Distance classifier-based model and 93.40% for Back Propagation Neural Network Classifier.
Breast cancer survival data prediction using machine learning model

Breast cancer is the most common form of cancer affecting women, exerting a significant impact on individuals, families, and societies globally. With its multifaceted nature, breast cancer research and awareness efforts have gained substantial momentum, leading to transformative breakthroughs in understanding its causes, diagnosis, treatment, and prevention. Survival analysis is a pivotal statistical tool in understanding the dynamic and often complex trajectory of breast cancer. As a disease that evolves, breast cancer research benefits immensely from survival analysis, which provides insights into patient outcomes, treatment efficacy, and the influence of various factors on survival. In this paper, Habermans Survival Dataset is used to analyze the data on breast cancer. The primary objective of this study is to establish the correlation between input and output variables, along with identifying significant features. The overarching aim of this research is to assess and compare the efficacy of various machine learning models in order to ascertain the optimal one.
PELATIHAN PEMBUATAN VIDEO PEMBELAJARAN MELALUI MICROSOFT POWERPOINT BAGI GURU SDN 7 RARANG KABUPATEN LOMBOK TIMUR

The goal that will be achieved through this community service is that teachers have the knowledge and skills to produce learning media in the form of learning videos made via Microsoft Power Point. Power Point is a simple choice because it is easy for teachers to operate and master. The methods used in training to make learning videos are lectures, discussions, questions and answers, and practice. The number of participants who attended the training was eleven participants. The training material consists of two, namely first, delivered by Hulaimi M.Pd. regarding the purpose and benefits of learning videos. Second, delivered by Nu'man, S.Pd., M.Pd. regarding how to make learning videos using Microsoft Power Point technically and practically. The training in making learning videos was highly appreciated by the school. This is very useful for teachers in the learning process and developing teacher professionalism in the current era. As a result of this training, several teachers succeeded in making learning videos.
The University Freshmen’s Perception in the Liberal Arts Course Selection Process: An Exploratory Study on Their Convergent Cultural Identity

This study examined the cultural characteristics of university freshmen’s perception organized in liberal arts course enrollment at this point when the COVID-19 pandemic has been gradually stabilizing. Their perception was divided into two categories; a single cognitive system, one of the characteristics of the culture of modernity and a multiple cognitive system, one of the characteristics of the culture of postmodernity. The former is divided into three theme clusters, learning contents as superficial explicit knowledge, pursuit of originality and authenticity from a course, and course enrollment based on formal collectivity. The latter is also divided into three clusters, learning contents as multi-layered tacit knowledge, pursuit of virtuality and pseudo-event from a course, and course enrollment based on situational individuality. The results show that the subjective consciousness, situation, and structural environment of freshmen who experienced the COVID-19 era and entered the university interacted in the construction of their convergent cultural identity. Universities need to develop and operate university liberal arts education that meets convergent values, considering the freshmen’s perception.
A Delphi Study on the Development of Global Citizens' Competency Indicators in the Local Community: Focusing on the Case of Y-gu, Incheon

The purpose of this study is to develop local community-level indicators that can monitor the capabilities of global citizens to measure the effectiveness of global citizenship education for adults amid the spread of global citizenship education. It followed the distinction of cognitive, social and emotional, and behavioral areas, which are the core competencies of global citizens presented by UNESCO. The categories were selected after reflecting the main conceptual words derived through prior research analysis and the preliminary survey results for related parties, and considering the major conceptual words necessary for Y-gu, Incheon. After that, an expert Delphi survey was conducted twice to develop indicators. The results of the study are as follows. A total of 46 indicators were developed, including 3 categories, 14 conceptual elements, and 17 questions in the cognitive domain, 3 categories, 13 concept elements, 15 questions in the social and emotional domain, and 3 categories, 10 concept elements, and 14 questions in the behavioral domain. Through this study, it was confirmed that global citizenship and local citizenship should be recognized from an integrated perspective, and that global problems should be viewed in connection with local daily lives. This study is meaningful in that it presented examples that can be appropriately used in other regions where global citizenship education is being conducted.
Conceptual and methodological foundations for improving foreign-language lexical competence based on self-educational activities of technical university students

The aim of the study is to theoretically substantiate and develop a model for improving lexical competence based on foreign-language self-educational activities of technical university students. The paper considers the essential and content-structural characteristics of foreign-language self-educational activities for the development of the lexical side of speech; compiles the nomenclature of self-educational strategies that determine technical university students’ ability to independently and effectively master foreign-language vocabulary. The scientific novelty of the work lies in the conceptual and methodological substantiation of the process of improving foreign-language lexical competence on the basis of self-educational activities of technical university students. As a result of the study, the authors have proposed a methodological model for improving lexical competence based on self-educational activities of technical university students.
Other Functions

While Power Query’s foundational functions empower you to navigate, shape, and aggregate data, there are moments when your data transformations demand an extra layer of sophistication. This is where the advanced functions come into play. They offer the ability to make nuanced decisions, handle exceptions gracefully, and craft custom expressions tailored to your specific needs.
Deep Learning Models to Estimate and Predict the Solar Irradiation in Brazil

Solar irradiation is the backbone of photovoltaic power technologies and its quantization allows to optimize energy generation. However, solar irradiation can be difficult to detect, mostly due to the design and disposition of sensors, as well as their high cost. To address this limitation, this paper proposes a deep neural network-based model to estimate global solar irradiation by only relying on weather data, focusing on applications targeting the Brazilian territory. The model uses a deep neural network trained with data from the Brazilian National Institute of Meteorology (INMET), which includes 606 nationwide weather stations and over 39 million hourly records of meteorological variables cataloged from years 2010 to 2022. Thus, in this paper i) a deep neural network is used to estimate irradiation, and ii) a long short-term memory is used to predict solar irradiation considering different time granularities: 5 min, 30 min, 6 h, and 1 day. The results show a small error between the measured irradiation data and the calculated results with regard to the following six meteorological variables: time, temperature, relative humidity, wind speed, precipitation, and atmospheric pressure. Moreover, experimental validations conducted using a weather station set up by the authors demonstrate that the proposed models can accurately predict solar irradiation. Thus, the developed model stands as a promising approach for applications within the Brazilian perspective, improving the efficiency and reliability of solar energy generation.
The Effect of Student Participation in Organizing HIMA Family Welfare Education on Student Learning Achievement

This research aims to determine: 1) an overview of the influence of participation in organizations at Hima PKK on student learning achievement; 2) learning achievement before and during organizing at Hima PKK; and 3) the influence of student participation in organizations on student learning achievement at Hima PKK. This research is a quantitative study using the ex post facto method. The sampling technique uses a saturated sampling technique. Data collection techniques using observation, questionnaires, and documentation The data analysis technique uses descriptive statistical analysis and inferential statistical analysis to explain the influence between variables. Based on the research results, it was found that 1) the picture of the influence of student participation in organizations on learning achievement is very well presented with four indicators: level of attendance at meetings, position held, providing suggestions, criticism, and opinions for improving the organization, Willingness of members to make sacrifices and member motivation 2) Learning achievement before and during the organization at Hima PKK is that out of 30 students, there are 20 students with the highest score with a very good predicate with a percentage of 66.6%, while those with the second highest score with a good predicate are 8 people with a percentage of 26.6%. 3) Based on data analysis, it shows that the influence of participation in organizing at Hima PKK on student learning achievement is at a low percentage of 22.9%. This shows that participation in organizations does not have a bad effect on student learning achievement because there are 77.1% other variables that influence it.
Planning and Construction of a Quantum Model of the SHA-1 Algorithm Using IBM’s Qiskit

The hash produced by a cryptographic hash function is inherently impossible to reverse-engineer or duplicate. You can protect yourself from hacking attempts more effectively with this. In a quantum environment, attacks on information systems would look significantly different, calling for a complete overhaul of current cryptographic practices. For the purpose of constructing a quantum-based Secure Hash Algorithm (SHA-1) from the ground up, a complete guide is provided to the design of quantum addition along with the associated circuits of XOR, AND, NOT, OR, MOD, CARRY, and SUM. To boost the efficiency of the standard SHA-1 algorithm, the q-SHA-1 is created in a Qiskit-based quantum environment. Experimental results from a wide range of conditions will be compared to evaluate the relative efficacy of the classical and quantum SHA-1 algorithms. However, it has been discovered that the execution time is highly dependent on the particular CPU type that is being used. A more powerful processor ought to cut down on the amount of time needed for the overall execution.
Generation Z's Perception of Teaching Methods for the Study of Physical Assessment in the Healthcare Professions.

It has been shown that an educational framework that combines different learning methods, in which students are both active and independent learners, significantly improves student achievement and satisfaction--and that this is especially true for Generation Z. For this study, we examined the following teaching methods in a Practical Kinesiology course: the flipped classroom, a digital app, and simulated patients. Our findings revealed that students greatly valued hands-on practice and advance learning, and this was borne out in the overall high final grades in the course and in self-reported confidence levels. Interestingly, regarding the technological component introduced in the course, students felt that the course app only moderately contributed to their learning, as students testified to the need for experiential learning to increase their confidence in performing clinical skills. The authors postulate, however, that the value of studying with the app will increase over time as students use it in the clinical setting. During the COVID-19 pandemic and moving forward, clinical courses in the health professions can most effectively be taught by using the flipped classroom model and by maximizing hands-on practical training.
The fable “The Star and the Comet” by V. A. Zhukovsky: The history of the text and meanings

The aim of the research is to identify the semantic component and artistic specificity of the images of celestial bodies in V. A. Zhukovsky’s poem “The Star and the Comet”. The paper is devoted to comparing Zhukovsky’s fable with a German source and considering the movement of poetic thought from the draft to the final version of the text; the mechanisms underlying the transformation of visible pictures of the universe into an artistic image with a high emotional impact are determined. The scientific novelty of the research consists in clarifying the factual basis of Zhukovsky’s writing of the fable “The Star and the Comet”, considering the creative history of the poem, interpreting it not only as a loose translation of the poetic study by Gottlieb Konrad Pfеffеl, but also as a result of superimposing the author’s own experimental observations of the movement of celestial bodies on the German source, which expands the understanding of cosmic imagery features in the aesthetics of Russian Romanticism. As a result of the research, the functional spectrum of the images of celestial bodies in Zhukovsky’s poem “The Star and the Comet” has been determined, the ratio of the national to the universal in these images has been revealed. Through textual and cultural-historical commentary, the mechanisms of poetic embodiment of the cosmos model as a fragment of the national worldview are traced, Zhukovsky’s handwritten materials are analyzed in relation to the intellectual realities of the 1820s.
Pembuatan Web Company Profile Sebagai Captive Portal Mikrotik Hotspot PT. Bukit Baiduri Energi

WiFi networks are very crucial in today's corporate industry. Because the WiFi network contains the internet which acts as a data transfer and communication route to support the company's operations. PT. Bukit Baiduri Energi (PT. BBE) as a company operating in the coal mining sector relies heavily on the internet to carry out company operations. Every day there is always an exchange of data and communication from employees to monitor the process of carrying out existing tasks and work, in responding to problems faced during the internship at PT. Bukit Baiduri Energi, our team takes the right steps to find the optimal solution. The first step taken was to identify the company's needs and formulate the idea of ​​creating a web company profile as a Mikrotik Hotspot captive portal. The website development process involves utilizing HTML, CSS, and JavaScript to create an attractive appearance and ensure functionality. The results of the project during the internship were to create a web company profile as a captive portal for PT Mikrotik Hotspot. Bukit Baiduri Energy. During the internship period at PT BBE, our team succeeded in completing the captive login hotspot project on Mikrotik devices by changing the design of the hotspot login portal and adding a brief company profile page. This project provides important benefits for companies in improving the security of their Wi-Fi networks and introducing the company to users through profile pages. The result is a positive step in supporting the growth and image of companies in the coal mining sector.
HARNESSING BLOCKCHAIN FOR SUSTAINABLE PROCUREMENT: OPPORTUNITIES AND CHALLENGES

The integration of blockchain technology into sustainable procurement processes has garnered significant attention in recent years, promising a transformative shift in ensuring transparency, traceability, and adherence to sustainability standards. This study embarked on a comprehensive exploration of the potential, effectiveness, challenges, and future implications of blockchain in the realm of sustainable procurement. Utilizing a rigorous methodology, the research delved into decentralized ledgers, smart contracts, and the inherent transparency offered by blockchain. Key findings highlighted the unparalleled potential of blockchain in enhancing trust and accountability among stakeholders, while also emphasizing the barriers to its widespread adoption, such as high implementation costs and the need for a paradigm shift in organizational culture. The study concludes with a forward-looking perspective, emphasizing the profound benefits organizations stand to gain by proactively embracing blockchain in their procurement processes. Recommendations underscore the importance of stakeholder education, pilot projects, and collaboration with regulatory bodies. As the landscape of sustainable procurement evolves, blockchain emerges as a pivotal tool in shaping its future.&#x0D; Keywords: Blockchain, Sustainable Procurement, Decentralized Ledgers, Smart Contracts, Transparency.
Nomogram Based on Super-Resolution Ultrasound Images Outperforms in Predicting Benign and Malignant Breast Lesions

Objective: To establish a good predictive model using a deep-learning (DL)-based three-dimensional (3D) super-resolution ultrasound images for the diagnosis of benign and malignant breast lesions. Methods: This retrospective study included 333 patients with histopathologically confirmed breast lesions, randomly split into training (N=266) and testing (N=67) datasets. Eight models, including four deep learning models (ORResNet101, ORMobileNet_v2, SRResNet101, SRMobileNet_v2) and four machine learning models (OR_LR, OR_SVM, SR_LR, SR_SVM), were developed based on original and super-resolution images. The best performing model was SRMobileNet_v2, which was used to construct a nomogram integrating clinical factors. The performance of nomogram was evaluated using receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and calibration curves. Results: SRMobileNet_v2, MobileNet_V2 based on super-resolution ultrasound images, had the best predictive performance in four traditional machine learning models and four deep learning models, with AUC improvements of 0.089 and 0.031 in the training and testing sets, relative to the ORMobileNet_v2 model based on original ultrasound images. The deep-learning nomogram was constructed using the SRMobileNet_v2 model score, tumor size, and patient age, resulting in superior predictive efficacy compared to the nomogram without the SRMobileNet_v2 model score. Furthermore, it demonstrated favorable calibration, discrimination, and clinical utility in both cohorts. Conclusion: The diagnostic prediction model utilizing super-resolution reconstructed ultrasound images outperforms the model based on original images in distinguishing between benign and malignant breast lesions. The nomogram based on super-resolution ultrasound images has the potential to serve as a reliable auxiliary diagnostic tool for clinicians, exhibiting superior predictive performance in distinguishing between benign and malignant breast lesions. Keywords: deep learning, radiomics, machine learning, breast lesion, super-resolution
NFTs, DAOs AND SMART CONTRACTS: WHERE IS THE DECENTRALISATION?

DAOs, decentralised autonomous organisations, you have the power, right? Well, like most other matters now-a-days, it depends. Oftentimes, the relationship between NFTs and DAOs is intertwined as a DAO may be set up with the purpose of creating NFTs or else, one may buy an NFT to become a DAO member. Both DAOs and NFTs make use of smart contracts on a blockchain. Thepurpose of this paper is to answer the following questions:How decentralised is a DAO? What is the role of NFTs within a DAO?How can NFTs enhance the workings of a truly decentralised autonomous organisation? The author believes that decentralisation should stop being used as a hype word in the blockchain sphere and discussesaboutcertain problems regardingcentralised and decentralised points in a DAO, whether theyrealised or not. Both the Maltese Innovative Technology Arrangement and Services Act and the Wyoming Decentralized Autonomous Organizations Supplement will serve as focus as theselaws directly regulate DAOs and in turn serve as a means to protect the member and the client of the DAO, and their NFTs.