Reviewerly/exHarmony huggingface dataset:
Prediction of Grade Point Average (GPA) for Students at Informatics and Computer Engineering Education – Universitas Negeri Jakarta during Online Learning Using Naive Bayes Algorithm

Authors:
raw_author_name: Miftahul Jannah
raw_affiliation_string: Program Studi Pendidikan Teknik Informatika dan Komputer, Fakultas Teknik,Universitas Negeri Jakarta
raw_author_name: Widodo Widodo
raw_affiliation_string: Program Studi Pendidikan Teknik Informatika dan Komputer, Fakultas Teknik, Universitas Negeri Jakarta
raw_author_name: Widodo Widodo
raw_affiliation_string: Program Studi Pendidikan Teknik Informatika dan Komputer, Fakultas Teknik, Universitas Negeri Jakarta
raw_author_name: Hamidillah Ajie
raw_affiliation_string: Program Studi Pendidikan Teknik Informatika dan Komputer, Fakultas Teknik, Universitas Negeri Jakarta
raw_author_name: Hamidillah Ajie
raw_affiliation_string: Program Studi Pendidikan Teknik Informatika dan Komputer, Fakultas Teknik, Universitas Negeri Jakarta

Abstract: 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.
Understanding OCR's Complaint and Resolution Process

Authors:
raw_author_name: Eric Lyerly

Abstract: 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.
Impact of The Covid-19 Pandemic on Student Learning Styles: Naïve Bayes and Decision Tree Classification in Education

Authors:
raw_author_name: Zaqi Kurniawan
raw_affiliation_string: Universitas Budi Luhur Jakarta
raw_author_name: Rizka Tiaharyadini
raw_affiliation_string: Universitas Budi Luhur Jakarta

Abstract: 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

Authors:
raw_author_name: Jonathan D. Gammell
raw_affiliation_string: Department of Engineering Science, Oxford Robotics Institute (ORI), Estimation, Search, and Planning (ESP) Research Group, University of Oxford, Oxford, UK

Abstract: 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

Authors:
raw_author_name: Carlos Juiz
raw_affiliation_string: Computer Science Department, University of the Balearic Islands, Palma de Mallorca, Spain
raw_author_name: Belen Bermejo
raw_affiliation_string: Computer Science Department, University of the Balearic Islands, Palma de Mallorca, Spain

Abstract: 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

Authors:
raw_author_name: Priyadarshini Chatterjee
raw_affiliation_string: Research Scholar, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India
raw_author_name: Shadab Siddiqui
raw_affiliation_string: Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, India
raw_author_name: Giuseppe Granata
raw_affiliation_string: Associate Professor, Business Management and Marketing, University Mercatorum, Rome

Abstract: 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

Authors:
raw_author_name: Qunpo Liu
raw_affiliation_string: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, Henan, China
raw_author_name: Jingwen Zhang
raw_affiliation_string: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, Henan, China
raw_author_name: Yi Zhao
raw_affiliation_string: School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, Henan, China
raw_author_name: Naohiko Hanajima
raw_affiliation_string: Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment, Jiaozuo, Henan, China

Abstract: 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)

Authors:
raw_author_name: Helena Juliana Kristina
raw_affiliation_string: Industrial Engineering Department, Universitas Tarumanagara, Jakarta, Indonesia
raw_author_name: Teuku Yuri M. Zagloel
raw_affiliation_string: Industrial Engineering Department, Universitas Indonesia, Depok, Indonesia
raw_author_name: Maslin Masrom
raw_affiliation_string: Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia Kuala Lumpur, Kuala Lumpur, Malaysia
raw_author_name: Sani Susanto
raw_affiliation_string: Industrial Engineering Department, Universitas Katolik Parahyangan, Bandung, Indonesia
raw_author_name: Agustinus Purna Irawan
raw_affiliation_string: Mechanical Engineering Department, Universitas Tarumanagara, Jakarta, Indonesia
raw_author_name: Vikas Kumar
raw_affiliation_string: Faculty of Business, Law and Social Sciences, Birmingham City University, Birmingham, UK
raw_author_name: Jose Arturo Garza-Reyes
raw_affiliation_string: Centre for Supply Chain Improvement, University of Derby Business School, Derby, UK
raw_author_name: Tji Beng Jap
raw_affiliation_string: Faculty of Information Technology, Universitas Tarumanagara, Jakarta, Indonesia

Abstract: 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

Authors:
raw_author_name: Muhasshanah Muhasshanah
raw_affiliation_string: Prodi Teknologi Informasi, Universitas Ibrahimy, Jawa Timur 68374, Indonesia

Abstract: 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.
Efficient Object Detection and Classification Approach Using an Enhanced Moving Object Detection Algorithm in Motion Videos

Authors:
raw_author_name: K. Madhan
raw_affiliation_string: Research Scholar, Department of Computer Science and Engineering, Dhanalakshmi Srinivasan University, Tamil Nadu, India
raw_author_name: N. Shanmugapriya
raw_affiliation_string: Associate Professor, Department of Computer Science and Engineering, Dhanalakshmi Srinivasan University, Tamil Nadu, India

Abstract: Object detection and classification have become prominent research topics in computer vision due to their applications in areas such as visual tracking. Despite advancements, vision-based methods for detecting smaller targets and densely packed objects with high accuracy in complex dynamic environments still encounter challenges. This paper introduces a novel and enhanced approach for hyperbolic shadow detection and object classification based on the Enhanced Moving Object Detection (EMOD) algorithm and an improved manta ray-based convolutional neural network optimized for search. In the preprocessing phase, the video data transforms into a sequence of frames, with polynomial adaptive antialiasing applied to maintain frame size and reduce noise. Additionally, an enhanced boundary area preservation algorithm improves the contrast of noise-free edited image sequences. To achieve high-precision detection of smaller objects, the Grib profile of each detected object is also tracked. Finally, a convolutional neural network method employing an enhanced Manta search optimization is deployed for target detection and classification. Comparative experiments conducted across diverse datasets and benchmark methods demonstrate significantly improved accuracy and expanded capabilities in detection and classification.
Modeling of Specialized Training Curricula

Authors:
raw_author_name: N. Vasil'chenko
raw_affiliation_string: Institute of Education Development Strategy of the Russian Academy of Education

Abstract: The article focuses on the need to modernize approaches to organizing specialized education in high school in connection with the change in vectors of economic and technological development of the country, which are reflected in the latest decisions of the President and government of the Russian Federation. To achieve this task, an organizational and content model of specialized training is presented, which makes it possible to combine the goals of participants in educational relations, including the state, and the requirements of the regulatory framework of Russian education. The result of this combination is a curriculum for specialized training, examples of the compilation of which for a technological profile of an engineering orientation complete the article.
Improved honey badger algorithm based on elementary function density factors and mathematical spirals in polar coordinate systema

Authors:
raw_author_name: Si-Wen Zhang
raw_affiliation_string: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, People’s Republic of China
raw_author_name: Jie-Sheng Wang
raw_affiliation_string: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, People’s Republic of China
raw_author_name: Yi-Xuan Li
raw_affiliation_string: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, People’s Republic of China
raw_author_name: Shi-Hui Zhang
raw_affiliation_string: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, People’s Republic of China
raw_author_name: Yu-Cai Wang
raw_affiliation_string: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, People’s Republic of China

Abstract: Abstract The Honey Badger Algorithm (HBA) is a new swarm intelligence optimization algorithm by simulating the foraging behavior of honey badgers in nature. To further improve its convergence speed and convergence accuracy, an improved HBA based on the density factors with the elementary functions and the mathematical spirals in the polar coordinate system was proposed. The algorithm proposes six density factors for attenuation states based on elementary functions, and introduces mathematical expressions of the polar diameters and angles of seven mathematical spirals (Fibonacci spiral, Butterfly curve, Rose spiral, Cycloid, Archimedean spiral, Hypotrochoid and Cardioid) in the polar coordinate system based on the density factors with the best synthesized effect to replace the foraging strategy of honey badger digging pattern in HBA. By using 23 benchmark test functions, the above improvements are sequentially compared with the original HBA, and the optimization algorithm with the best improvement, α4CycρHBA, is selected to be compared with SOA, MVO, DOA, CDO, MFO, SCA, BA, GWO and FFA. Finally, four engineering design problems (pressure vessel design, three-bar truss design, cantilever beam design and slotted bulkhead design) were solved. The simulation experiments results show that the proposed improved HBA based on the density factors with the elementary functions and the mathematical spirals of the polar coordinate system has the characteristics of balanced exploration and expiration, fast convergence and high accuracy, and is able to solve the function optimization and engineering optimization problems in a better way.
Parasitic Reflection Eliminating for Planar Elements Based on Multi-Frequency Phase-Shifting in Phase Measuring Deflectometry

Authors:
raw_author_name: Siya Huang
raw_affiliation_string: College of Electronics & Information Engineering, Sichuan University, Chengdu 610065, China
raw_author_name: Yuankun Liu
raw_affiliation_string: College of Electronics & Information Engineering, Sichuan University, Chengdu 610065, China
raw_author_name: Xin Yu
raw_affiliation_string: College of Electronics & Information Engineering, Sichuan University, Chengdu 610065, China

Abstract: Phase measuring deflectometry (PMD) stands as an extremely important technique for specular surface measurement. However, the parasitic reflection from the rear surface poses a challenge for PMD. To solve this problem, this paper proposes an effective method based on multi-frequency and phase-shifting to search for the correct phase. Firstly, the relationship between the phase error and fringe frequency is adequately investigated. Subsequently, an auxiliary function is established to find the special frequency at which the phase error is zero theoretically and the unwrapped phase is the phase of the top surface exactly. Then, the shape of the top surface can be reconstructed correctly. A standard plane element with a thickness of 40 mm and a flat glass with 19 mm were measured. The experimental results verify the feasibility of the proposed method. Considering the result of the interferometer as a reference, the RMSE of the error map is up to 20 nm for the standard plane element. The experimental results demonstrate that the proposed method can successfully untangle the superposed reflections and reliably reconstruct the top surface of the object under test.