Reviewerly/exHarmony huggingface dataset:
Student Loan: Topic Modelling with Twitter Data

The study is about citizens opinions on student loans by analyzing Twitter reactions to Bidens student loan cancellation project using the machine-driven classification of open-ended response (MDCOR) and found it saved research time, increased efficiency, and ensured authenticity and objectivity of data. After putting data into the application, we found that using five analysis topics is appropriate. The topics content can be predicted by seeking the relevant word for each case. The analysis of five issues related to student loans shows mixed opinions about the impact of loan forgiveness, with some key terms such as predatory and donation being significant. At the same time, some topics are not directly related to the issue.
Management of Dynamic Archives for Education Personnel in State Madrasah Aliyah

This research aimed at knowing 1) creating dynamic archives in the administrative section, and 2) storing dynamic archives in the administrative section at State Islamic Senior High School 3 Pekanbaru. It was a qualitative research with a descriptive method. The subjects of this research were the head of administration, administrative staff as key informants, and the vice of student affairs and teachers as additional informants. The object of this research was the management of archives in the administration section at State Islamic Senior High School 3 Pekanbaru. Interview, observation, and documentation techniques were used for collecting the data. The data were analyzed by using reduction, presentation, and conclusion. The results of this research show that: 1) The creation of dynamic archives in the administration section of MAN 3 Pekanbaru was carried out well, including the process of incoming and outgoing letters. 2) The storage of dynamic archives in the administration section of MAN 3 Pekanbaru was carried out well, however, there were a few obstacles in the archive retrieval process, namely archives or letters that have been disposed of and are located outside the administration room, such as the principal's room, the deputy principal's room for student affairs and curriculum, forgetting to take them back, so there are archives that are not stored in the filing cabinet.
Deep Surveillance System

This study has been undertaken to investigate and implement multiple detection systems into a single surveillance system and check whether the input videos may comprise and capture a variety of realistic anomalies or not. In this paper, we propose to learn various anomalies by exploiting both normal and anomalous videos and implemented it to new model. Real time object detection is a vast, vibrant and sophisticated area of computer vision aimed towards object identification and recognition. Object detection detects the semantic objects of a class objects using Open source Computer Vision, which is a library of programming functions mainly trained towards real time computer vision in digital images and videos. The main aim behind this real time object detection is to help the peoples to overcome their difficulty. Real time object detection finds its uses in the areas like tracking objects, video surveillance, pedestrian detection, people counting, self-driving cars, face detection, tracking in sports and many more. This is achieved using Convolution, Probabilistic Neural Networks, etc. which are a representative tool of Deep learning. This project acts as an aiding tool for peoples who wants to take care of everything inside, outside, and around their house just for their full security expectations. Surveillance is a must for small houses to large-scale industries as they fulfil our safety aspects because theft and burglary have always been a problem. By combining this Surveillance idea to IoT and some Machine Learning stuff this will be a major product. The proposed project is a single autonomous surveillance system, based on analysis and detection technology. The proposed system is capable of monitoring all actions at once and alerts the concerned officials immediately and precisely.
Pengembangan Media Pembelajaran Flipbook Digital Panduan Praktikum Sequence Of Service Pada Mata Kuliah Tata Hidang

The aim of this study is to develop a digital flipbook practikum guide of the sequence of service on course food and beverage service. Digital flipbook practical guide is much needed in practical learning because it contains multimedia elements which can be used personally by college students. This media is developed by research and development (RnD) method, employing 4D Thiagarajan, Semmel and Semmel 1974 model. The stages of this RnD study are define, design, develop, and desseminate. The result of this learning media consist 3 part is preliminary, core, and closing. this media has been validated by material expert and media expert, they value 82% for material which mean that it is very feasible and 88% for media which mean that it is very feasible. the average score of both this validation is 85% which is fall category very feasible. So it can be concluded that the digital flipbook practikum guide of the sequence of service is very feasible on course food and beverage service
Mathematical competencies with applications of mirror classes in regular basic education

&lt;span lang="EN-US"&gt;The problem about learning in mathematics lies in the lack of application of teaching strategies for the resolution of the calculation in mathematical problems, problems of movement, mathematical regularity, equivalence, and uncertainty; in populations with difficulties to learn collaboratively. Here the application of mirror classes in populations with interactive problems is demonstrated, thus contributing to strengthening their knowledge from cooperative information management, the use of analysis skills based on co-evaluation. In this sense, the purpose of the study was to evaluate the mirror class, as a pedagogical strategy, in order to optimize the competence of solution of quantity problems in basic school students of two educational institutions in Lima, Peru. The study was conducted under the action research approach where the diagnostic and exit test, question guide and field diary were the instruments used for data collection. It was concluded that the mirror class as a pedagogical strategy favors the optimization of competition and solves quantity problems in basic education students. So, they also developed concrete knowledge, to assign them in cognition more enduringly as a form of social learning.&lt;/span&gt;
Online cognitive assessment in the era of COVID-19: Examining the validity of the MEZURE.

Developed more than 2 decades ago, the MEZURE (Assessment Technologies, 1995-2020; https://www.mezure.com/) has received increased attention as a result of the COVID-19 pandemic. It is the first individualized test of cognitive ability created to use an online (local or remote) assessment modality. The MEZURE claims to be aligned both with the extended Gf-Gc theory and the Cattell-Horn-Carroll model of abilities. Whereas the test publisher claims it used exploratory factor analysis to investigate the instrument's factor structure, only the subtest factor loadings on the Gf-Gc factors were furnished. No other structural validity information was provided, suggesting that users of the instrument should interpret the scores produced by the MEZURE with caution. Accordingly, the present study used exploratory and confirmatory factor analysis to more fully investigate the structural validity of the MEZURE. The results revealed that the MEZURE contains a combined perceptual reasoning (i.e., [Gf/Gv]/working memory [Gwm]) group factor, a verbal ability group factor, and a relatively weak general factor that is dominated by perceptual reasoning. The finding of a paltry general factor that is weakly loaded by verbal subtests is inconsistent with the broader research on traditional cognitive ability assessment and could be related to the online administration format of the test. Future research is required to better understand this finding. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
The Structural Relationship among Perception of K-MOOC, Teaching Presence and University Life Satisfaction: Multi-group Analysis according to Participation in K-MOOC

Objectives The purpose of this study is to examine the structural relationship among K-MOOC perception, teach-ing presence, and university life satisfaction, and to analyze them according to participation in K-MOOC courses using a multi-group analysis. The analysis was conducted using Pearson's correlation coefficient to examine the relationships between variables, and the trends and normality were assessed.&#x0D; Methods The subjects of the study are 244 students at Chungcheong-do University who are taking K-MOOC courses by 2022. It was analyzed by the multi-group structural equation and the structural relationship among var-iables using the SPSS 25.0 and AMOS 23.0 programs.&#x0D; Results These are the main results. First, there was a positive correlation between perception of K-MOOC, teach-ing presence and university life satisfaction. Second, the measurement model was deemed suitable, and teaching presence was found to be a complete mediator in the process of K-MOOC perception affecting university life satisfaction. third, the results of multi-group analysis based on participation in K-MOOC courses showed that ‘teaching presence -&gt; university life satisfaction’ had a stronger influence on students who took K-MOOC courses than those who didn't, proving the partial adjustment effect.&#x0D; Conclusions The results of this study are meaningful in that they provide basic data to raise awareness of K-MOOC. In addition, we would like to propose a plan to activate K-MOOC, focusing on universities and departments.
An Efficient Blockchain-based Auditing Scheme for EHR in Intelligent Healthcare System

Electronic health records (EHRs) are health records that are digitally stored and consist of data about a person’s wellness and increasing gradually. To store the EHR, a cloud service provider provides the storage for storing the EHR. By storing the data on the cloud, the integrity of data is a big concern for the EHR owner. Many Blockchain-based data integrity schemes are proposed for data integrity verification in which binary Merkle tree and quad Merkle tree are used, which increases the verification time for the EHR. To reduce the verification time in integrity auditing, the proposed scheme uses the ternary Merkle tree-based Blockchain for integrity auditing. The experiment and results show that verification time is reduced to 3 times lesser than the quad Merkle tree-based Blockchain and also reduces the execution time to 3.5 times lesser than the previous scheme. The security analysis shows that the proposed scheme is secured against some of the potential threats.
A Supply Chain Finance Framework Based on Zero-Trust Architecture

The post-epidemic era has brought to light significant issues in traditional supply chain financing, particularly with regard to the financing challenges faced by SMEs. Despite the considerable advancements made in the supply chain financing model, it appears that the existing framework is inadequate in meeting the financing requirements of SMEs. The root cause of this inadequacy can be attributed to the inherent challenges posed by the chain's structure, such as lengthy credit evaluation cycles, fixed evaluation nodes, and the inability to promptly reflect the needs of downstream enterprises. In the realm of computers, the zero-trust architecture is a highly valuable framework. This paper presents a comprehensive analysis of the supply chain financing framework, with a focus on enhancing its efficacy through the implementation of a zero-trust architecture, and a quality management framework is proposed to complement the proposed architecture.
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains

Domain generalization (DG) is a difficult transfer learning problem aiming to learn a generalizable model for unseen domains. Recent foundation models (FMs) are robust to many distribution shifts and, therefore, should substantially improve the performance of DG. In this work, we study generic ways to adopt contrastive languageimage pre-training (CLIP), a visual-language foundation model, for DG problems in image classification. While empirical risk minimization (ERM) greatly improves the accuracy with bigger backbones and training datasets using standard DG benchmarks, fine-tuning FMs is not practical in many real-world situations. We propose Domain Prompt Learning (DPL) as a novel approach for domain inference in the form of conditional prompt generation. DPL achieved a significant accuracy improvement with only training a lightweight prompt generator (a three-layer MLP), whose parameter is of equivalent scale to the classification projector in the previous DG literature. Combining DPL with CLIP provides surprising performance, raising the accuracy of zero-shot CLIP from 73.7% to 79.3% on several standard datasets, namely PACS, VLCS, OfficeHome, and TerraIncognita. We hope the simplicity and success of our approach lead to broader adoption and analysis of foundation models in the domain generalization field.
Secure quantum communication using trusted third party

A quantum internet is one of the promises of the second quantum revolution. New difficulties arise when the first quantum network (QN) hardware prototypes get closer to completion. The development of scalable QN systems is still in its early stages, yet a functioning network is more than simply the physical tools. The proposed method offers a QN protocol that addresses the novel conceptual and technological difficulties posed by quantum physics and enables end-to-end quantum communication. As the key procedure in quantum information science, quantum entanglement swapping (QES) has the potential to revolutionise QN communication. The suggested paradigm, which considers entanglement, enables safe communication utilising a trusted third party across a long-distance QN. The protocols might act as the basic structure of a future quantum internet. Even if scaling and entanglement coherence issues must be overcome, this research provides the groundwork for improving error-correcting codes and fault-tolerant quantum computers.
Comparative Analysis of Surrogate Models for High-Dimensional Expensive Problems

To resolve the challenges posed by evaluating approximated fitness values in high-dimensional expensive optimization problems. This work primary focus on exploring the theoretical and structural aspects of four distinct surrogate models. The models under comparison are radial basis function, Broad learning system, random vector functional link neural networks, and extreme learning machines. The primary focus of the investigation encompassed the comprehensive examination of six distinct test functions, applied to both 100-D and 50-D benchmark problems. Throughout the comparison, meticulous attention was directed towards two pivotal facets: accuracy and computational cost. On the other hand, the combination of the approximated fitness values and the approximated uncertainty is regarded as two distinct objectives.The study's results revealed notable differences in performance among the examined models. Radial basis function and random vector functional link neural networks exhibited superior accuracy and scalability compared to the Broad learning system and extreme learning machines. Among these, the random vector functional link neural networks show-case the best performance across all six test functions in the 50-D benchmark problems. The implications of these findings are valuable for selecting appropriate fitness estimation models in the context of High-dimensional Expensive optimization problems.
Selective Kernel Fusion Complex-Valued CNN for Modulation Recognition

Automatic modulation recognition (AMR) plays an essential role in intelligent communication networks monitoring, management, and optimization. Recently, it has been shown that deep learning-based methods perform well in AMR. However, most existing methods are based on real-valued networks, e.g., convolution neural network (CNN), which are not specifically designed for AMR. Thus, the recognition performance is limited. In this paper, we propose a selective kernel fusion complex-valued convolution neural network (SKF-CCNN) for the fulfillment of the AMR task. The proposed method uses parallel complex-valued convolution for raw in-phase/quadrature (I/Q) sequence together with real-valued convolution for amplitude. The complex-valued features and amplitude features are then fused by a selective kernel block to combine all sorts of information. Lastly, a ResNeXt block and two convolution layers are employed to extract further information from the fused feature map for the task of the final classification. Experiments on the benchmark dataset show that the proposed method outperforms the existing complex-valued methods, especially at high SNRs.
Optimization of Personal Credit Evaluation Based on a Federated Deep Learning Model

Financial institutions utilize data for the intelligent assessment of personal credit. However, the privacy of financial data is gradually increasing, and the training data of a single financial institution may exhibit problems regarding low data volume and poor data quality. Herein, by fusing federated learning with deep learning (FL-DL), we innovatively propose a dynamic communication algorithm and an adaptive aggregation algorithm as means of effectively solving the following problems, which are associated with personal credit evaluation: data privacy protection, distributed computing, and distributed storage. The dynamic communication algorithm utilizes a combination of fixed communication intervals and constrained variable intervals, which enables the federated system to utilize multiple communication intervals in a single learning task; thus, the performance of personal credit assessment models is enhanced. The adaptive aggregation algorithm proposes a novel aggregation weight formula. This algorithm enables the aggregation weights to be automatically updated, and it enhances the accuracy of individual credit assessment by exploiting the interplay between global and local models, which entails placing an additional but small computational burden on the powerful server side rather than on the resource-constrained client side. Finally, with regard to both algorithms and the FL-DL model, experiments and analyses are conducted using Lending Club financial company data; the results of the analysis indicate that both algorithms outperform the algorithms that are being compared and that the FL-DL model outperforms the advanced learning model.
Prediction of Solar Energy Production Quantity Using AR

Studies related to energy efficiency in solar power plants generally focus on strategies to enhance the performance of solar energy systems, factors influencing the efficiency of solar panels, and various methods for optimizing energy production. In this study, a stepwise regression analysis was employed to predict the amount of energy generated in solar power plants. Independent variables such as weather conditions, ambient temperature, and panel efficiency were examined for their impact on energy production. Stepwise regression was used to model this relationship in a step-by-step manner. The purpose of the study is to analyze the impact of relevant independent variables on energy production and to present the estimation of the most optimal prediction model based on these analysis results.
Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

INTRODUCTION: Sports video is a very important information resource. The classification of sports video with high accuracy can effectively improve the browsing and query effect of users. This project intends to study a motion video classification algorithm based on deep learning particle filter to solve the problems of strong subjectivity and low accuracy of existing motion video classification algorithms. A key box extraction method based on similarity is proposed. The moving video classification algorithm based on deep learning coding model is studied. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can greatly improve the classification performance of motion video.
Transformation of Type I Fuzzy Logic into Interval Type II Fuzzy Logic-based Unit Voltage Template with Controlled Load

The interval type II fuzzy logic controller (IT2FL) is a recent artificial intelligent technology which is applied in many aspects of electrical power engineering and controlling strategies these days. The improving of Distribution Static Synchronous Compensator (DSTATCOM) control algorithms is still ongoing, because the control strategy of DSTATCOM device is considered as a decisive factor which affects the performance of DSTATCOM device in enhancing the quality of power in the distribution grids. The improving process focuses in two main aspects the DC capacitor voltage response to the control system and the mitigation of the harmonics which are produced by the controlled and uncontrolled rectified loads. In this paper, the interval type II fuzzy logic controller (IT2FL) based the Unit Voltage Template strategy (UVT) is examined by the MATLAB program with two different ranges of input signals, and compared with the type I Fuzzy logic controller (T1FL). The IT2FL based UVT shows promising results in improving the deformed shape of source current waves in comparison with the other controllers.
Enhancement of various images using coefficients obtained from a class of Sakaguchi type functions

Abstract Digital image processing has a wide range of uses, including robotics and automated inspection of industrial parts. Other uses include remote sensing using satellites and other spacecraft, image transmission and storage for business applications, medical processing, and Acoustic image processing. The process of highlighting particular intriguing features in a hidden image is known as image enhancement. We can accomplish this by altering the brightness, contrast, etc. The generated output is more suitable than the original image for some particular purposes. The proposed algorithm which is based on the convolution of coefficient bounds of a subclass $$p-\Upsilon {\mathcal {S}}^*(t,\delta ,\mu )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>-</mml:mo> <mml:mi>Υ</mml:mi> <mml:msup> <mml:mrow> <mml:mi>S</mml:mi> </mml:mrow> <mml:mo>∗</mml:mo> </mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>,</mml:mo> <mml:mi>δ</mml:mi> <mml:mo>,</mml:mo> <mml:mi>μ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> obtained using Mittag-Leffler type Poisson Distribution is tested on three image data sets with different dimensions and image formats (PNG, JPEG, TIFF, etc.) and its PSNR, SSIM, MSE, RMSE, PCC and MAE values are observed to check the quality of the enhanced images.