Reviewerly/exHarmony huggingface dataset:
Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking

In order to increase a nonlinear system’s state estimate precision, an iterated orthogonal simplex cubature Kalman filter (IOSCKF) is presented in this study for target tracking. The Gaussian-weighted integral is decomposed into a spherical integral and a radial integral, which are approximated using the spherical simplex-radial rule and second-order Gauss–Laguerre quadrature rule, respectively, and result in the novel simplex cubature rule. To decrease the high-order error terms, cubature points with appropriate weights are taken from the cubature rule and processed using the provided orthogonal matrix. The structure supporting the nonlinear Kalman filter incorporates the altered points and weights and the calculation steps; from this, the updated time and measurement can be inferred. The Gauss–Newton iteration is employed repeatedly to adjust the measurement update until the termination condition is met and the IOSCKF is attained. The proposed algorithms are applied in target tracking, including CV target tracking and spacecraft orbit tracking, and the simulation results reveal that the IOSCKF can achieve higher accuracy compared to the CKF, SCKF, and OSCKF. In spacecraft orbit tracking simulation, compared with the SCKF, the position tracking accuracy and velocity tracking accuracy of the OSCKF are increased by 2.21% and 1.94%, respectively, which indicates that the orthogonal transformation can improve the tracking accuracy. Furthermore, compared with the OSCKF, the position tracking accuracy and velocity tracking accuracy of the IOSCKF are increased by 2.71% and 2.97%, respectively, which indicates that the tracking accuracy can be effectively improved by introducing iterative calculation into the measurement equation, thus verifying the effectiveness of the method presented in this paper.
Influence of Brand Image on Customer Loyalty: A Look from Bangladesh

The aim of this research is to explore the influence of brand image on customer loyalty especially in reference to banking companies in Bangladesh. Brand awareness and brand satisfaction are the two sub‐ constructs which are used to represent the brand image of the companies. The study served a structured questionnaire and collected data from 363 respondents from the clients of ten commercial banks of Bangladesh. Employing the structural equation model (SEM) with SmartPls, the study found significant influence of brand awareness and brand satisfaction on the customer loyalty of the surveyed banks. The findings of this study may be of interest to the high bank officials as well as to the institutional policy makers to revise the branding policy as marketing strategy for achieving sustainable growth.
PSSegNet: Segmenting the P- and S-Phases in Microseismic Signals through Deep Learning

Microseismic P- and S-phase segmentation is an influential step that limits the accuracy of event location, parameter inversion, and mechanism analysis. Therefore, an improved Unet named PSSegNet is proposed to intelligently segment the P- and S-phases. The designed masks are used as the outputs of PSSegNet, which is used to obtain the time–frequency features of the P- and S-phases. As a result, the MSE (mean square error) between the predicted mask and the actual labeled mask is concentrated below 2.5, and the AE (accumulated error) of the reconstructed P/S-phase based on the predicted mask is concentrated below 1.0 × 10−3. Arrival picking results show that the overall error of the entire test set is less than 50 ms and most of the errors are less than 20 ms. Data with SNR (signal to noise ratio) &lt; 2, 2 ≤ SNR &lt; 3, PSR (P-phase to S-phase ratio) &lt; 1, or 1 ≤ PSR &lt; 2 in the dataset were selected for arrival picking and their errors were counted. The statistical results show that PSSegNet is robust at low SNR and PSR. The P- and S-phase segmentation based on PSSegNet has excellent potential for use in various applications and can effectively reduce the difficulty of obtaining the P/S-phase arrivals.
Exploring The Two Decades Of Quality Assurance Advancement In Vocational Education And Training

Quality assurance in vocational education and training (VET) becoming a concern driven by evolving educational landscapes and the demand for skilled and job-ready individuals. This study presents a comprehensive bibliometric analysis of research on VET quality assurance from the year 2003 to 2022. Data was visualized using VOSviewer and Tableau. This paper aims to provide insights into the quantitative distrubution, citation pattern, geographical dispersion, and emerging topics in this field. Results show an increasing volume of publications on quality assurance in VET. The citation pattern suggests that the depth of exploration and engagement with existing knowledge is not proportionally robust. Russian Federation emerges as a prominent contributor in terms of document output. A lack of international research collaboration among countries, signaling a need for increased global engagement. The identified trends in research can be categorized into three clusters. Cluster 1 focuses on aspects related to the implementation of quality assurance in VET, Cluster 2 delves into methodological approaches in this context, and Cluster 3 explores research contexts associated with the value, benefits, or objectives of implementing quality assurance in VET. This article also offers a reflection on the findings as research that necessitates further exploration. This article can serve as a valuable resource for empirical researchers seeking to examine the temporal evolution of study participants and the shifting research themes pertaining to quality assurance in vocational education.
The Implementation of Rewards and Punishment on the Students' Motivation in English Learning at Junior High School of SMPN 7 Mataram Academic Year 2022/2023

This research aimed to find out the implementation of rewards and punishment on the students’ motivation in English learning at eighth grade students’ of SMPN 7 Mataram in academic year 2022/2023. This study used a descriptive qualitative approach. The sampling technique consisted purposive sampling, which included 4 English teachers, and 40 students. The data was collected involved observations, interviews, and questionnaires. The researcher utilized data collection, data reduction, data display, and conclusion as analysis techniques. The results showed that the teachers respond positively to the students who showed a lack of motivation by conducting monitoring students’ behavior and their participation during learning process. Rewards and punishment were applied should be consider the condition and situation occurred in the classroom. Meanwhile, the teachers applied punishment by understanding the characteristics of each student so they did not feel uncomfortable, or disturbed their psychological. giving rewards made the students more diligent was 70%. 95% students were happy with rewards. Meanwhile, 87.5% students more responsible, and 82.5% students obey the rules and behave well. Therefore, it can be inferred that learning motivation has increased, and the students’ undesirable behavior has decreased.
Methods for Tapping the Competence of Hindi (L2) Learners

Adopting an appropriate methodology for eliciting data from students learning a foreign or second language (L2) has always been a tough task for tapping the competence of young learners’ before they lose interest in the data elicitation tasks requires researchers to be skillful and perceptive. This work stems from the research study conducted by Kumar (2017). It highlights the methodological procedure followed for collecting data from (L1) Korean students who were part of the Semester Aboard program at the Department of Linguistics, University of Delhi for learning Hindi (L2) as a foreign language from August to December 2016.
KONTRIBUSI SISTEM INFORMASI MANAJEMEN DALAM MENINGKATKAN MUTU PENDIDIKAN

The management information system is a set of information systems that provide output using an input as well as various processes needed to achieve the objectives contained in an educational management process. With the existence of a management information system, the preparation of information can be easily arranged properly so that it is in accordance with the wishes of the customer. In addition, the contribution of management information systems can assist in presenting precise and accurate data as well as fast and flexible. For this reason, it can be understood that the information management system has a very large contribution to improving the quality of education. This study aims to analyze and determine the contribution of management information systems in improving the quality of education. This study uses the method of library analysis by utilizing library resources to obtain theories and limiting activities only to library collection materials and other factual references.
RGB-D Tracking via Hierarchical Modality Aggregation and Distribution Network

The integration of dual-modal features has been pivotal in advancing RGB-Depth (RGB-D) tracking. However, current trackers are less efficient and focus solely on single-level features, resulting in weaker robustness in fusion and slower speeds that fail to meet the demands of real-world applications. In this paper, we introduce a novel network, denoted as HMAD (Hierarchical Modality Aggregation and Distribution), which addresses these challenges. HMAD leverages the distinct feature representation strengths of RGB and depth modalities, giving prominence to a hierarchical approach for feature distribution and fusion, thereby enhancing the robustness of RGB-D tracking. Experimental results on various RGB-D datasets demonstrate that HMAD achieves state-of-the-art performance. Moreover, real-world experiments further validate HMAD’s capacity to effectively handle a spectrum of tracking challenges in real-time scenarios.
Reservoir Computing System Based on VCSEL with the Self-Polarization-Stabilization Structure

We propose a reservoir computing system with a self-polarization-stabilization structure and demonstrate the performance improvement through the simulation task. Furthermore, we analyze the performance of the VCSEL-based reservoir in two other simulation tasks.
Age and Gender Estimation Through Speech: A Comparison of Various Techniques

Age and gender recognition from audio signals have gained significant attention due to their potential applications in various domains, including personalized user experiences, targeted marketing, biometric identification, demographic analysis, and healthcare. This research paper presents a comparative analysis of age and gender recognition models using three different audio features: pitch, Mel-frequency cepstral coefficients (MFCC), and spectral dynamic features (SDC). For age recognition, the performance of Support Vector Machines (SVM), k-Nearest Neighbors (kNN), Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), and Gaussian Naive Bayes (GNB) classifiers was evaluated. The SVM classifier with a linear kernel achieved the highest accuracy (96%) and precision (99%) using the SDC feature. In the case of gender recognition, SVM with the RBF kernel outperformed other classifiers, achieving an accuracy of 97.5% and an F1 score of 97.3% using the pitch feature.
Hyperbolic Tangent Sigmoid as a Transformation Function for Image Contrast Enhancement

Contrast enhancement is critical for investigating and highlighting important hidden features in a computer vision system. Continuous functions, such as incomplete beta or sigmoid functions, have traditionally been used for histogram equalization. However, histogram equalization cannot uniformly enhance the local contrast of an image, which is its main limitation. In this study, we investigate a contrast enhancement method based on a hyperbolic tangent sigmoid whose parameters can be optimized by metaheuristics. In our study, we investigated the performance of three popular metaheuristics when coupling the proposed hyperbolic tangent sigmoid to find the optimal pixel values that can intensify features of low-contrast images. The proposed method is studied on a public domain image dataset and evaluated using standard performance indicators. Preliminary results show that the proposed hyperbolic tangent sigmoid can improve image contrast and quickly adapt to other metaheuristics.
Security Provisioning in Quantum Key Distribution-Secured Optical Networks

We consider the security of quantum keys and encrypted data in QKD-secured optical networks. A security framework integrating a multi-path multi-protocol (2MP) service provisioning scheme is proposed to enhance network security and reduce network costs.
Design of Improvised DCM-Based Tunable True Random Number Generator

Emphasizing the critical role of True Random Number Generators (TRNGs) in contemporary cryptographic systems, particularly when integrated with Field Programmable Gate Arrays (FPGAs), this work introduces an exceptionally efficient and adaptable TRNG based on Beat Frequency Detection (BFD) principles. Tailored for Xilinx FPGA applications, this TRNG offers on-the-fly tunability through Dynamic Partial Reconfiguration (DPR) to enhance randomness quality. Empirical findings from the TRNG's implementation on a Xilinx Virtex-V FPGA are provided, along with an explanation of the mathematical concept behind its operations. This TRNG stands out because of its small hardware footprint and built-in bias removal features. Notably, it creates random bitstreams that pass every test in the NIST statistical test suite, demonstrating its dependability and applicability for cryptographic applications.
Turbo Powered Symmetric and Asymmetric Traffic Encryption Methods in Multi-Cloud Environment

In this current digital world, the term “Cloud” represents limitless possibilities. It refers to a large network of computers that use the Internet to store, process, and manage data and services. This provides unmatched scalability and accessibility, and it is cost-efficient. Most simply, it is the delivery of on-demand IT Resources like storage, network, and databases of our choice over the internet. However, the adaptation of cloud resources has raised problems like data security, compliance, data privacy, downtime, and availability of the cloud. This article mainly focuses on the challenges in cloud security and presents some of its probable solutions to reduce the risks caused by cloud security issues. The research study starts out by outlining cloud security and going over the various cloud security concerns. The best behaviors for protecting cloud data are further explained, including using strong passwords and multi-factor authentication, encrypting data while it's in transit and at rest, as well as putting access control policies. By recognizing these problems, and by applying the most effective security measures, organizations/businesses can safely take advantage of cloud computing.
Mainapproach - Artificial Intelligence over on Earth Science

The powerful planet on which we all live is Earth. Actually have individuals begun to sort out the complexity of this planet. In the field of natural sound observing, the common estimation is the demonstrates the typical A-weighted Sound Strain Level. This is easy to see yet gives no genuine detail on the substance of the sound scene, which can be key in its effect on those encountering it. Man- made brainpower thinking techniques have demonstrated to areas of strength for be a course of action of Earth predictable fields. Perhaps of the most quickly making in the field of electronic data advancement. In a grouping of Earth research. I'm exploring on reproduced insight enabled progresses like sharp inversion, splendid sensors, and light dealing with to the test. These movements could possibly help with lessening earth vibration by 15 august 2030 Key Words: Earth Science, Earth Vibration, Light Sensor, Earth Spine.
Autoencoder-LSTM Algorithm for Anomaly Detection

As the field of Artificial Intelligence (AI) continues to expand, AI-driven anomaly detection algorithms become paramount for operators to issue corrective actions, preventing disasters and reducing unnecessary costs. Historically, AI utilized deterministic rule-based techniques for anomaly detection. Today advances in AI have enabled more sophisticated algorithms. This paper proposes an Autoencoder Long Short-Term Memory (AE-LSTM) algorithm to improve anomaly detection. We evaluate and compare the efficacy of AE-LSTM against the benchmark Deep Neural Network Long Short-Term Memory (DNN-LSTM) algorithm. Evaluation metrics include false positives (FP), false negatives (FN), algorithm execution/run time, and F1-score. Autoencoder (AE) base architecture has been chosen to leverage its dimension reduction capabilities for relevant feature extraction. Our proposed scheme results show a considerable improvement from DNN-LSTM for anomaly detection which applies directly to space application and operation and was evaluated based on real-world datasets.
Unifying Genetics and Imaging: MRI-Based Classification of MGMT Genetic Subtypes Using Visual Transformers

Brain cancer can take many forms, but glioblastoma (GBM) is one of the most aggressive. To treat it effectively, doctors need to know the genetic subtype of a specific part of the tumor called the O-6-methylguanine-DNA-methyltransferase (MGMT) promoter. This information is critical for making treatment decisions and predicting outcomes. However, traditional methods for predicting MGMT status have significant limitations. One of the main reasons for this poor performance is that these methods fail to capture small variations in MRI signatures that are essential for accurately predicting MGMT promoter methylation status. In this work, we propose a two-part approach to predict the MGMT methylation status. First, we use autoencoders to extract features from the MRI images, capturing intricate spatial patterns and representations. Then, we feed these encoded features into a visual transformer architecture to learn long-range dependencies and contextually significant relationships among image regions. This innovative fusion of autoencoders and visual transformers allows us to harness both local and global information present in the MRI data. We utilized a dataset consisting of 1010 3D MRI images from patients with GBM. Half of the patients had MGMT promoter methylation, while the other half did not. The model achieved impressive results, including an overall accuracy score of 0.904, recall score of 0.902, precision score of 0.914, F1 score of 0.906, and specificity score of 0.906. These metrics far surpass the accuracy of conventional imaging techniques.
Rekhta: An Open-Source High-Quality Urdu Text-to-Speech Synthesis Dataset

A dataset is one of the most pivotal components in creating and developing Deep Learning and Machine Learning models. It is fundamental to the idea of training, refining, and evaluating AI models, serving as a metric for evaluating the model's results. As an outcome of advancements in innovation, research, and AI analysis, datasets in numerous languages and formats have emerged on the internet. However, the expanse of internet resources appears to contract when confronted with Machine Learning and Deep Learning tasks in Urdu. The sheer availability of resources and authenticated datasets in the Urdu language, juxtaposed with a multitude of subpar, incomplete, and unverified datasets, significantly hinders the advancement of AI in the Urdu language. This paper documents the exploration and refinement of the Common Voice Urdu Corpus dataset version 12.0 to create a clean and refined dataset suitable for training Urdu Text-to-Speech models. This task has been accomplished through primary research, preliminary analysis, techniques such as fuzzy string matching through Levenshtein Distance, and meticulous comparison among various models.
Image Encoding and Recovery Based on Inhibited Spiking Dynamics of VCSEL Neuron

We propose and numerically investigate an image encoding and recovery system based on the inhibited spiking dynamics of a 1550-nm vertical-cavity surface-emitting laser (VCSEL) neuron. The results demonstrate that VCSELs subject to orthogonal optical injection can mimic the behaviors of biological neurons under external perturbations (stimuli) with different intensities and durations. Furthermore, VCSEL neuron can possess the encoding capability of binary-to-spike with GHz rate. Based on the post-processing techniques of spiking dynamics, the image recovery with bit error rate (BER) of 6.6× 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> is achieved.
Comparative Study of Elastic Net Regression, Naive Bayes &amp; Lasso Regression

This study aims to determine which algorithm is better at estimating target variables based on a set of independent variables. This case study compares three different machine learning algorithms: Naive Bayes, Lasso Regression and Elastic Net Regression. The data used in this research is real-world data with many features. The article begins by explaining how each algorithm works and then evaluates it on various parameters such as Accuracy, Recall, and Prediction. The results of the study shows that Naive Bayes outperforms Lasso Regression and Elastic Net in terms of accuracy and precision, while Elastic Net has the highest return. The article concludes that the choice of algorithm depends on the problem at hand and that researchers should consider all parameters before choosing an appropriate algorithm.
Optimalisasi Evaluasi Hasil Belajar menggunakan Aplikasi Kahoot! pada Guru Sekolah di Lingkungan Yayasan Wakaf Al Choir Pangeran Jayakarta

Teachers in schools within the Prince Jayakarta Al Choir Waqf Foundation have not used application-based technology in evaluating learning outcomes. So far, learning evaluations have been carried out using paper. This makes the process of evaluating learning outcomes long and the results are not immediately known to students. The aim of this community service is to train teachers to use the Kahoot! to assist teachers in evaluating learning outcomes so that the teacher's work can be optimal. The evaluation results can also be received immediately by all students, and the analysis of the questions can be known right away. The method for implementing this service is by conducting effective training directly using a computer connected to an internet network with stages, (1) Presentation, the material is explained clearly, concisely and concisely, (2) demonstration, the material is demonstrated directly and participants see it, (3) ) Direct practice, participants immediately practice the material presented, (4) Mentoring, participants are accompanied while doing practice, (5) Demonstration, participants' work is demonstrated in front of the class, and (6) Using Google Classroom to find out the progress of learning outcomes, discussions, and ongoing assistance. The result of this service activity is that participants can use the Kahoot! to evaluate learning outcomes starting from creating an account, creating various types of questions by entering text, images, formulas, and running them, as well as finding out the results and evaluating the questions on the spot. Participants are greatly helped in making evaluations of learning outcomes that are interesting, easy and fast.
Analisis Clustering Kasus Covid – 19 di Indonesia dengan Kelompok Umur 40-60 Tahun menggunakan Algoritma K-Means

The Covid-19 virus is a case that first occurred in Wuhan City, China, which attacks the respiratory system, resembling influenza or flu which attacks the lungs. This research has the aim of being a class analysis to find out how the spread of the virus occurred in Indonesia. This study uses the K – Means method or the median value in calculations based on age in all Indonesian provinces for the last 3 years. The results of the study obtained the highest distribution in the age group 40-60 years in 2020, so it can be said that the decline in Covid - 19 in 2021 has decreased in analysis carried out using the Rapid Minner software.