Reviewerly/exHarmony huggingface dataset:
Prediction of permeability in a tight sandstone reservoir using a gated network stacking model driven by data and physical models

Introduction: Permeability is one of the most important parameters for reservoir evaluation. It is commonly measured in laboratories using underground core samples. However, it cannot describe the entire reservoir because of the limited number of cores. Therefore, petrophysicists use well logs to establish empirical equations to estimate permeability. This method has been widely used in conventional sandstone reservoirs, but it is not applicable to tight sandstone reservoirs with low porosity, extremely low permeability, and complex pore structures. Methods: Machine learning models can identify potential relationships between input features and sample labels, making them a good choice for establishing permeability prediction models. A stacking model is an ensemble learning method that aims to train a meta-learner to learn an optimal combination of expert models. However, the meta-learner does not evaluate or control the experts, making it difficult to interpret the contribution of each model. In this study, we design a gate network stacking (GNS) model, which is an algorithm that combines data and model-driven methods. First, an input log combination is selected for each expert model to ensure the best performance of the expert model and selfoptimization of the hyperparameters. Petrophysical constraints are then added to the inputs of the expert model and meta-learner, and weights are dynamically assigned to the output of the expert model. Finally, the overall performance of the model is evaluated iteratively to enhance its interpretability and robustness. Results and discussion: The GNS model is then used to predict the permeability of a tight sandstone reservoir in the Jurassic Ahe Formation in the Tarim Basin. The case study shows that the permeability predicted by the GNS model is more accurate than that of other ensemble models. This study provides a new approach for predicting the parameters of tight sandstone reservoirs.
Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification

The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.
Staged cluster transformers for intracranial aneurysms segmentation from structure fused <scp>3D MRA</scp>

Abstract Intracranial aneurysms (IAs) manifest as atypical dilatation within the intracranial arterial structures, the rupture of which accounts for high mortality and morbidity rates. Current clinical protocols require radiologists to manually annotate IAs on Magnetic Resonance Angiography (MRA) images, which is inherently subjective and time‐consuming. Given these limitations, there is an urgent need to explore methods for automated and accurate segmentation of IAs from MRA images. In particular, recent years have witnessed the proliferation of sophisticated computational techniques, with deep learning algorithms—especially the 3D U‐Net and its derivatives—gaining prominence in segmentation works. Nevertheless, convolutional neural network (CNN)‐based models have an inherent limitation in capturing long‐range spatial dependencies, which inadvertently compromises the retention of global features critical for segmentation. In response to this challenge, we introduce an avant‐garde architectural design, dubbed staged cluster transformers (SCTR), which incorporates cluster mechanism into vision transformers to perform volumetric MRA image segmentation. In addition to the MRA clustering branch, the spatially aligned brain Magnetic Resonance Imaging (MRI) representation branch is also combined to extract the structural features and assist the network in learning richer contextual and boundary information for accurate voxel prediction. For validation, we utilized both a publicly available challenge dataset and an internal clinical dataset in this study. Our proposed model achieves dice similarity coefficients (DSC) of 0.5587 and 0.8110 on these two datasets, respectively, outperforming other state‐of‐the‐art approaches. The results suggest that SCTR is a promising method for automatic segmentation of IAs. Our code is available at https://github.com/guolilin/SCTR .
Classification Comparison Performance of Supervised Machine Learning Random Forest and Decision Tree Algorithms Using Confusion Matrix

The classification method is part of data mining which is used to predict existing problems and also as predictions for the future. The form of dataset used in the classification method is supervised data. The random forest classification method is processed by forming several decision trees and then combining them to get better and more precise predictions. while a decision tree is the concept of changing a pile of data into a decision tree that presents the rules of a decision. From these two classification methods, researchers will compare the level of accuracy of predictions from both methods with the same dataset, namely the employee dataset in India, to predict the level of accuracy of employees who leave their jobs or still remain to work at their company. The number of records available is 4654 records. Of the existing data, 90% was used as training data and 10% was used as test data. From the results of testing this method, it was found that the accuracy level of the random forest method was 86.45%, while the decision tree method was 84.30% accuracy level. Then, by using the confusion matrix, you can see the magnitude of the distribution of experimental validity visually to calculate precision, recall and F1-Score. The random forest algorithm obtained precision of: 96.7%, sensitivity of: 84.7%, specificity of: 91.4%, and F1-Score of: 90.2%. Meanwhile, the decision tree algorithm obtained precision of: 95.7%, sensitivity of: 82.9%, specificity of: 88.4%, and F1-Score of: 88.8%.
OCR concerned with different treatment of students with disabilities

Case name: Letter re: Central Community School District 301 , No. 05‐23‐1246 (08/17/23).
Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm

The transition of learning models from face-to-face to online learning has had several impacts on student learning, reflected in their academic achievements. This study aims to determine the performance of the algorithm model using data mining classification techniques in predicting the Semester Grade Point Average (GPA) of Informatics and Computer Engineering Education students, at Universitas Negeri Jakarta during online learning. The prediction employed the Naive Bayes algorithm and the dataset obtained by collecting questionnaires from 2020 and 2021 batches. The total data obtained is 155 records with 13 (thirteen) attributes in the form of 1 (one) ID attribute including NIM, 11 (eleven) regular attributes including gender, college entrance, smartphone facilities, network conditions, preferred online applications, interest in learning, learning attitudes, learning creativity, parental support, study groups, and other activities outside of lectures during online learning, and 1 (one) the label attribute namely the Semester Grade Point Average for students in 3rd and 5th semester. The evaluation of this research involved the confusion matrix and the ROC (Receiver Operating Characteristic) curve. Confusion matrix resulted in an accuracy of 75%, precision of 28.33%, and recall of 26.43%. The ROC curve resulted in an AUC value of 0.679, indicating the category of poor classification. This study also applied the SMOTE data balancing technique, leading to a confusion matrix evaluation with 88.46% accuracy, 57.43% precision, and 52.14% recall. Furthermore, the ROC curve resulted in an AUC value of 0.809 which is categorized as a Good classification.
Classification of Student Grade Data Using the K-Means Clustering Method

The fourth industrial revolution has brought significant changes in various sectors, and education has been greatly affected by technological advances. Automation, particularly in data processing, has simplified educational processes, particularly in managing student grade data. However, the increasing volume of data poses challenges in efficient processing. This research explores the application of K-Means clustering, a data mining technique, to cluster student grade data. This research uses the Elbow Method to determine the optimal number of clusters. The dataset, sourced from the Information Systems Study Program at the Telkom Institute of Technology Purwokerto, includes attributes such as Credits Taken, GPA, Number of Ds, Number of Es, and Credits Not Taken. The results identified three groups of students: "High Achievers," "Average Performance," and "Needs Improvement." Recommendations include academic challenges for high performers, better learning methods for average performers, and remedial programs for those who need improvement. This research demonstrates the efficacy of K-Means clustering in improving educational strategies and support systems based on student characteristics.
Stakeholder Cues, National Origin, and Public Opinion Towards Firms: Evidence in the Context of the First Bank in an American Indian Nation

Abstract When and how does stakeholder credibility matter in shaping public opinion? We explore this question in a real-world setting: in order to fight its citizens’ financial exclusion—a key barrier to development in Indian Country—American Indian Nation “A” negotiated the first entry of the first bank to its reservation. The bank is owned by American Indian Nation “B.” To the Federal Reserve, the bank branch is a potential proof-of-concept for the capacity of tribe-to-tribe investment to improve capital access in underserved Native communities. The bank’s success ultimately depends on whether Nation A’s citizens use its services; in the months before its opening, all three stakeholders independently attempted to influence public opinion toward the bank. We collaborated to conduct a first-of-its-kind survey of Nation A’s tribal members, finding high baseline buy-in especially given the bank’s nationality, but weak and even counterproductive treatment effects of pro-banking cues provided by Nation A and the Federal Reserve. Our results make clear the practical benefits of theory-building around stakeholder credibility, and the crucial role of individual attitudes in the political economy of development.
Understanding OCR's Complaint and Resolution Process

The Office for Civil Rights is the enforcement arm of the United States Department of Education. OCR enforces numerous federal civil rights laws, including Section 504 and Title II of the ADA, that prohibit discrimination. To this end, the agency investigates complaints filed by students or other persons against their educational institution. This article explores frequently asked questions about the OCR complaint process.
The influence of work environment and work motivation on employee performance

This research aims to determine the influence of the work environment and work motivation on employee performance at PT XYZ Central Jakarta, both partially and simultaneously. The method used is quantitative. The sampling technique used saturated sampling, and the sample obtained in this study was 75 respondents. Data analysis uses validity tests, reliability tests, classical assumption tests, regression analysis, correlation coefficient analysis, coefficient of determination analysis, and hypothesis testing. The results of this research show that the work environment has a significant effect on employee performance. Work motivation has a substantial impact on employee performance. The work environment and work motivation simultaneously significantly affect employee performance. The correlation coefficient value is 0.823, meaning that the independent and dependent variables have an extreme level of relationship.
Impact of The Covid-19 Pandemic on Student Learning Styles: Naïve Bayes and Decision Tree Classification in Education

The Covid-19 pandemic significantly changed education with social distancing and changes in the learning environment. In this study, one strong reason for the significance of the research is the urgency of changes in students' learning styles during the Covid-19 pandemic. Investigating differences in learning styles before and during the pandemic not only provides deep insight into students' adaptation to these changes, but also provides a foundation for the development of more inclusive and adaptive learning strategies in the future. This study aims to analyze the effect of the Covid-19 pandemic on students' learning styles in an educational context, focusing on the comparison of two classification methods, Naïve Bayes and Decision Tree. The study was conducted by collecting data on students' learning styles before and during the Covid-19 pandemic, using various relevant indicators. The data was obtained based on school survey results and online platforms, involving student characteristics and learning preferences. The data was then analyzed using Naïve Bayes and Decision Tree classification methods to identify significant changes in students' learning styles. The results showed the prediction accuracy of learning style changes with Naïve Bayes 68.75% and Decision Tree 87.50%. Recommendations for educators and education policy makers are to develop inclusive and adaptive learning strategies to meet diverse learning preferences.
The surface edge explorer (SEE): A measurement-direct approach to next best view planning

High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the Next Best View (NBV) planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer (SEE), a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm.
On the scalability of the speedup considering the overhead of consolidating virtual machines in servers for data centers

Abstract Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.
Performance Analysis of Five U-Nets on Cervical Cancer Datasets

Image segmentation is crucial for precise analysis and classification of biomedical images, especially in the realm of cervical cancer detection. The accuracy of segmentation significantly influences the efficacy of subsequent image classification processes. While traditional algorithms exist for image segmentation, recent advancements in convolutional neural networks, particularly U-Nets have showcased exceptional effectiveness, especially in the realm of biomedical imaging. This research focuses on evaluating the accuracy of various U-Net architectures applied to three distinct cervical cancer datasets i.e., DSB containing 1340 images, SipakMed containing 1849 images and Intel Images for Screening containing 2000 images datasets taken from 2018 Data Science Bowl. The investigated U-Net architectures comprise the fundamental U-Net, Attention U-Net, Double U-Net, Spatial Attention U-Net, and Residual U-Net. The performance of the u-nets is judged on the metrics: Recall, Precision, F1, Jaccard and Accuracy. It is observed that Basic U-Net on the DSB dataset provides highest value on these metrices and accuracy obtained is 96%. The reason of high accuracy for DSB dataset can be attributed to the contrast of the images which by using co-occurrence matrix is calculated as 145.
A YOLOX Object Detection Algorithm Based on Bidirectional Cross-scale Path Aggregation

Abstract To solve the problem of insufficient feature fusion between the deep and shallow feature layers of the original YOLOX algorithm, which resulting in a loss of object semantic information, this paper proposes a YOLOX object detection algorithm based on attention and bidirectional cross-scale path aggregation. First, an efficient channel attention module is embedded in the YOLOX backbone network to reinforce the key features in the object region by distinguishing between the importance of the different channels in the feature layer, thus enhancing the detection accuracy of the network. Second, a bidirectional cross-scale path aggregation network is designed to change the information fusion circulation path while increasing the cross-scale connections. Weighted feature fusion is used to learn the importance of the different path input features for differentiated fusion, thereby improving the feature information fusion capability between the deep and shallow layers. Finally, the SIOU loss function is introduced to improve the detection performance of the network. The experimental results show that on the PASCAL VOC2007 and MS COCO2017 datasets, the algorithm in this paper improves mAP by 2.32% and 1.53% compared with the original YOLOX algorithm, and has comprehensive performance advantages compared with other algorithms. The mAP reaches 99.44% on the self-built iron ore metal foreign matter dataset, with a recognition speed of 56.90 frames/s.
The improvement of block chain technology simulation in supply chain management (case study: pesticide company)

Abstract This research was conducted on industrial agriculture in Indonesia. Risk analysis was carried out based on previous research. One source of risk was obtained, namely raw materials that did not meet specifications, which was then proposed to be mitigated by evaluating supplier performance. This activity involves a lot of data, requiring efficient and effective data storage and access. The level in the simulation layout includes analysing system needs, using problem diagrams, compiling activity diagrams, deciding subprocesses, and filtering information. The analysis is carried out by comparing the use of supply chains with Blockchain and without Blockchain, which is then obtained to determine whether there is an increase. A sequentially stored data scenario describes a situation when the transaction process is in progress and is stored sequentially according to the process that occurs. Storing data in groups explains a problem when a transaction has been completed and stored in groups with similar data, making it easier to track specific data. In this regard, a simulation will be carried out using a website, namely a blockchain demo. The design stage starts with identifying system requirements, creating use case diagrams, compiling activity diagrams, determining subprocesses, and selecting information. The simulation results obtained will be analysed to determine the feasibility of Blockchain as a means of supporting risk mitigation related to data using aspects, including security, trust, traceability, sustainability, and costs.
Rancang Bangun Sistem Informasi Laporan Keuangan Bulanan Pesantren di Kantor Kepesantrenan Putri

This research is the design of a monthly financial reporting information system for Islamic boarding schools at the Girls' Islamic Boarding School office, which was built using the PHP programming language. This information system will henceforth be named SIM-KA (Financial and Budget Management Information System). SIM-KA is accessed using the local network of the women's boarding school office. The financial reporting and budgeting of female Islamic boarding schools is still less than optimal, and errors often occur in the amounts reported. It is hoped that the design of this information system will (1) help Islamic boarding school administrators in reporting and making Islamic boarding school budgets so that they are more optimal, (2) help speed up work in making monthly Islamic boarding school reports and budgets, and (3) reduce errors in calculating recapitulation reports and budgets.