Reviewerly/exHarmony huggingface dataset:
Modeling Data Mining Algorithm for Predicting Timely Student Graduation at State University Surabaya

Information technology aims to advance human activities. Even now it has penetrated into the field of education, as a process and industrial sector, education cannot be separated from the field of information and technology. In the implementation of the academic process, the State University of Surabaya, which has 30,000 students, faces several obstacles in predicting the students' graduation time on time. The information system owned has not been used optimally to predict the time of student graduation. It is difficult to predict because this university does not have an exact time prediction pattern to use as a basis for predicting the number of students who will graduate on time. In helping these problems, the researcher provides a solution, namely building a data mining algorithm model to predict the exact time of graduation for Surabaya State University students. The data used are Strata-1 (S1) PTI, SI and TI students from 2014-2018. The methodology in this study is FAST (Framework For The Application Of System Thinking) using the Neural Network (NN) algorithm 761 student data with the input value of the artificial neural network method 4, hidden layer 5 and output 2 providing an accuracy of 99.85%. Details of late predictions are 98.06% as many as 101 students and correct predictions are 99.85% as many as 657 students. Shows that the Neural Network can be used as a prediction of the graduation of Strata-1 (S1) PTI, SI, and IT students at the State University of Surabaya.
Transformation of literacy program implementation based on digital learning platform in the era of disruption in Indonesia

The utilization of digital learning platforms is the real transformation of technology and information used in the era of disruption. This study aims to reveal how teachers and students respond to the use of digital learning palatform-based literacy programs at the elementary in Padangsidimpuan City, Indonesia. The research method applied was mix method with sequential exploratory approach which began qualitative and continued with quantitative research. The sampling technique used cluster random sampling method for quantitative and snowball sampling for qualitative research. Quantitative research data were taken from 5 schools with 150 students and 30 teachers. Techniques Data collection was carried out through the distribution of questionnaires for quantitative and in-depth interviews, documentation studies and observations for qualitative. The results shown that the results of quantitative research strengthen the results of qualitative research where the transformation of the use of online learning platforms gets good response from teachers and students. The use of online learning platforms consisting of whatsapp, drive, youtube, google classroom, and zoom via android could increase the effectiveness and efficiency of learning implementation.
SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution

The image Super-Resolution (SR) technique has greatly improved the visual quality of images by enhancing their resolutions. It also calls for an efficient SR Image Quality Assessment (SR-IQA) to evaluate those algorithms or their generated images. In this paper, we focus on the SR-IQA under deep learning and propose a Structure-and-Perception-based Quality Evaluation (SPQE). In emerging deep-learning-based SR, a generated high-quality, visually pleasing image may have different structures from its corresponding low-quality image. In such case, how to balance the quality scores between no-reference perceptual quality and referenced structural similarity is a critical issue. To help ease this problem, we give a theoretical analysis on this tradeoff and further calculate adaptive weights for the two types of quality scores. We also propose two deep-learning-based regressors to model the no-reference and referenced scores. By combining the quality scores and their weights, we propose a unified SPQE metric for SR-IQA. Experimental results demonstrate that the proposed method outperforms the state-of-the-arts in different datasets.
Skilling People in Nepal: Reality Vs Dream

Skilling people is channeled through formal, informal or non-formal education systems in Nepal. Formal education includes structured curriculum and mode of delivery where knowledge and skills are acquired in a hierarchical layer. Non-formal education includes the skill development activities in a less structured patterns, whereas imparting knowledge and skills from generation to generation falls under the category of informal mode of education. Similarly, skills development in formal system includes four different types of programs in Nepal. These are short-term training program, professional development program, secondary level technical and vocational education program (including diploma level program) and technical higher education program. This paper opens discussion on these programs, uncovers issues and challenges and suggests some measures for the improvement of program design and implementation.&#x0D; While preparing the paper, secondary documents are analyzed. In some cases, author's experiences are also used. This article is expected to contribute knowledge to the skill development efforts in Nepal.
THE EFFECT OF SELF REGULATED LEARNING, HIGH SCHOOL MAJORS, AND NATIONAL EXAMINATION SCORES ON ACADEMIC ACHIEVEMENT OF ACCOUNTING STUDENTS IN JAKARTA

The purpose of this research is to determine the impact of self-regulated learning (SRL), high school majors and national examination scores on academic achievement of accounting students in Universities in Jakarta. The data is collected with questionnaires with random sampling method and the samples obtained are 150 samples. The method of analysis is by using multiple linear regression. The results show that there is no significant relationship between self-regulated learning (SRL) and academic achievement (AA), high school majors (HSM) show results that have a negative and significant effect on academic achievement, while for the national examination scores (NES), it can be concluded that there is significant positive relationship between the national examination scores and academic achievement.
Misclassification Analysis of Elementary School Accreditation Data in Ambon City Using Multivariate Adaptive Regression Spline

Many classification methods have been developed, one of which is the Multivariate Adaptive Regression Spline (MARS) method. MARS is one of the classification methods in the form of a combination of Recursive Partitioning Regression (RPR) and the spline method that is able to process high-dimensional and large-sized data and process data with continuous or binary response variables. The purpose of this study was to measure the misclassification of elementary school accreditation in Ambon city using the MARS method. This study uses accreditation data with the results of eight components of accreditation in elementary schools that have accreditation A (group 1) and accreditation B (group 2) in Ambon city. To evaluate the classification method used the APER classification error measure. The best classification result from the MARS method is when using a combination of BF=32, MI=3, MO=1 because it produces a minimum Generalized Cross Validation (GCV) of 0.066 and information is obtained that the correct classification data is 181 and the misclassified data is 10. Based on the results of the analysis, the size of the APER classification error is 5.23%, which can be said that the MARS method is good or statistically significant for classifying elementary schools in Ambon City based on their accreditation rating.
EPIPOLAR GEOMETRY BETWEEN PHOTOGRAMMETRY AND COMPUTER VISION – A COMPUTATIONAL GUIDE

Abstract. Stereo image orientation is one of the major topics in computer vision, photogrammetry, and robotics. The stereo vision problem solution represents the basic element of the multi-view Structure from Motion SfM in computer vision and photogrammetry.A successfully reconstructed stereo image geometry is based on solving the epipolar constraint using the fundamental matrix which is based on the projective geometry in computer vision. However, in photogrammetry, the problem is well known as relative orientation and there is a different solution that is based on the euclidean geometry using collinearity or coplanarity equations.A lot of literature and discussions were found in the last decades to solve the epipolar geometry problem. However, there is still no clear description to compare between solutions introduced using both projective and euclidean solutions and which method of the relative image orientation is mostly preferred.To the best of our knowledge, computing and plotting the epipolar lines using photogrammetric collinearity and coplanarity equations is not shown before in the educational litrature. In this paper, a detailed mathematical solution of the epipolar geometry will be shown using both photogrammetric and computer vision techniques. This is aimed to remove any confusion for new learners in using the current methods in both scientific fields and show that using any technique should lead to comparable results with advantages and disadvantages.
Distribution image of prime numbers in natural numbers

This paper presents a method for drawing an image of the distribution of prime numbers in natural numbers.
Experimental Evidence for Using a TTM Stages of Change Model in Boosting Progress Toward 2FA Adoption

Behavior change ideas from health psychology can also help boost end user compliance with security recommendations, such as adopting two-factor authentication (2FA). Our research adapts the Transtheoretical Model Stages of Change from health and wellness research to a cybersecurity context. We first create and validate an assessment to identify workers on Amazon Mechanical Turk who have not enabled 2FA for their accounts as being in Stage 1 (no intention to adopt 2FA) or Stages 2-3 (some intention to adopt 2FA). We randomly assigned participants to receive an informational intervention with varied content (highlighting process, norms, or both) or not. After three days, we again surveyed workers for Stage of Amazon 2FA adoption. We found that those in the intervention group showed more progress toward action/maintenance (Stages 4-5) than those in the control group, and those who received content highlighting the process of enabling 2FA were significantly more likely to progress toward 2FA adoption. Our work contributes support for applying a Stages of Change Model in usable security.
Evaluating Uncertainty Calibration for Open-Set Recognition

Despite achieving enormous success in predictive accuracy for visual classification problems, deep neural networks (DNNs) suffer from providing overconfident probabilities on out-of-distribution (OOD) data. Yet, accurate uncertainty estimation is crucial for safe and reliable robot autonomy. In this paper, we evaluate popular calibration techniques for open-set conditions in a way that is distinctly different from the conventional evaluation of calibration methods on OOD data. Our results show that closed-set DNN calibration approaches are much less effective for open-set recognition, which highlights the need to develop new DNN calibration methods to address this problem.
Comparing Open Arabic Named Entity Recognition Tools

The main objective of this paper is to compare and evaluate the performances of three open Arabic NER tools: CAMeL, Hatmi, and Stanza. We collected a corpus consisting of 30 articles written in MSA and manually annotated all the entities of the person, organization, and location types at the article (document) level. Our results suggest a similarity between Stanza and Hatmi with the latter receiving the highest F1 score for the three entity types. However, CAMeL achieved the highest precision values for names of people and organizations. Following this, we implemented a "merge" method that combined the results from the three tools and a "vote" method that tagged named entities only when two of the three identified them as entities. Our results showed that merging achieved the highest overall F1 scores. Moreover, merging had the highest recall values while voting had the highest precision values for the three entity types. This indicates that merging is more suitable when recall is desired, while voting is optimal when precision is required. Finally, we collected a corpus of 21,635 articles related to COVID-19 and applied the merge and vote methods. Our analysis demonstrates the tradeoff between precision and recall for the two methods.
Face Attendance System

Attendances are taken in every school and college. The convention attendance system consists of registers marked by teachers which may lead to human error and a lot of maintenance. The system proposed in this study is to deviate from such a traditional system and introduce a new approach to taking attendance using image processing. The system uses a Histogram of Oriented Gradients (HOG) and python libraries such as OpenCV, Dlib, and NumPy. As a human, the brain automatically recognizes a face instantly, but the computer is not capable of this high-level generalization. The system automatically starts taking snaps and then applies face detection and recognition technique to the given image the recognized students are marked as present and their attendance is updated with the corresponding time[2]. The working of the system is that it first looks at a picture and finds all the faces in it. second, it focuses on each face and can understand that even if a face is directed in a weird direction or under bad lighting. Third, the system comes up with 68 specific points called landmarks, that exist on every face example the top of the chin, the inner edge of each eyebrow, etc., and then picks out unique features of the face that can be used to tell it apart from other people. Finally, compare the unique features of that face to those already determined faces. Then the person can clock in into the system after the person clocks out, the system automatically transfers the data into an excel sheet.
An Unsupervised Method Based on Unpaired Multimodality Data for Heterogeneous Face Recognition

Abstract Current deep learning methods for heterogeneous face recognition (HFR) rely on pairwise multimodal image data for training, but such data are difficult to collect. In this paper, we propose an unsupervised deep learning method based on unpaired multimodal image data. This method employs a variational autoencoder (VAE) and a discriminator from a generative adversarial network (GAN) to disentangle the given heterogeneous image data into domain-independent semantic features and domain-dependent style features. Specifically, the VAE utilizes its latent space to disentangle features and encode explicitly domain-independent semantic features that are used to match face images from different modalities. The discriminator is used to discriminate the domains of images generated by the VAE, which can improve the domain recognition ability of the VAE. Moreover, multiple-scale feature aggregation is incorporated into the encoder part of the VAE to make the domain-independent semantic features contain multiple-scale construction information. Experimental results obtained on three widely used face datasets are presented to demonstrate the effectiveness of the proposed method. Our code will be available on GitHub.
UI/UX Design in E-Learning University of Harapan Medan using User Centered Design (UCD)

The development of information technology that is used as a learning medium in the form of e-learning (Electronic Learning) has become an educational system that is quite important and is often used today. University of Harapan Medan uses e-learning as a learning medium, but it has not been fully utilized. Therefore, e-learning requires an updated and developed user interface design to improve the user experience. The system design uses the User-Centered Design (UCD) method, which focuses on the user as the center of each design process. The approach with this method can provide advice on interface design on an e-learning website as a solution according to user needs
Mobile Health Tracker Application for Children (Telehealth): Applying Soft System Methodology for Community IT-based Project

The main objective of this study is to apply the soft system methodology for Community IT-Based project.The method involved using action research.Postgraduate students in Information Technology from a public university in Malaysia taking the problem-solving course for information technology had applied the soft system methodology in their group project as part of their learning activities.The case study selected was an operating shelter home for the orphaned, the abused, abandoned, and neglected children in Selangor, Malaysia.The outcome of the project was a proposed prototype called Mobile Health Tracker Application for Children (Telehealth) that supports Sustainable Development Goals on Quality Education and Good Health and Well-Being.
GraphCache: Message Passing as Caching for Sentence-Level Relation Extraction

Entity types and textual context are essential properties for sentence-level relation extraction (RE).Existing work only encodes these properties within individual instances, which limits the performance of RE given the insufficient features in a single sentence.In contrast, we model these properties from the whole dataset and use the dataset-level information to enrich the semantics of every instance.We propose the GRAPHCACHE (Graph Neural Network as Caching) module, that propagates the features across sentences to learn better representations for RE.GRAPHCACHE aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences.The global property features act as dataset-level prior knowledge for RE, and a complement to the sentence-level features.Inspired by the classical caching technique in computer systems, we develop GRAPHCACHE to update the property representations in an online manner.Overall, GRAPHCACHE yields significant effectiveness gains on RE and enables efficient message passing across all sentences in the dataset.
A CROSS-SECTORAL APPROACH TO STEM EDUCATION: A MULTI-PERSPECTIVE DESIGN

The complexity of the STEM education ecosystem at all the educational levels has been risen by the COVID-19 pandemic. The search for a sustainable ecosystem leads to the cross-sectoral approach to STEM education. A sustainable STEM education ecosystem is also characterized by the coherence between the STEM education levels. The research aims at creating a theoretical model of the multi-perspective design of STEM education within a cross-sectoral approach for identifying the implications for further research on the cross-sectoral STEM education. The qualitative research was carried out. Research data were collected through the analysis of published research works. Content analysis was used for the analysis of the collected data. The theoretical novelty of the cross-sectoral STEM education is indicated: the structure and phases of STEM Education implementation and multi-perspective design. The exploratory research allows widening the traditional boundaries of the STEM education (teacher-content-student) with the concepts of educational discipline, conceptual change, all language as the unity, the digitalised educational process, and the cross-sectoral approach. The research is novel in the implications for further research on the cross-sectoral STEM education..
The “Gandalf” soil sampling project at a former industrial site in Copenhagen, Denmark: evaluating soil classification reliability

Handling and transport of contaminated soil from industrial sites in Denmark requires classification based on concentrations of selected metals and organic contaminants. Reliable soil classification is needed for defensible remedial decision-making. Today’s sampling process in Denmark is based on grab sampling of prescribed standard volumes of soil; 30 tons is typically used as the basic sampling Decision Unit. Soil classification follows a number of varying systems, but classification into five classes (class 0 to class 4) based on analytical results from sub-samples of 50 g is the most common. In this study we investigate the sampling uncertainty obtained by sampling of &gt; 1800 samples at a former industrial site in Copenhagen, Denmark. The aim of the study was to conduct a critical assessment of the current sampling strategy by determination of soil classification errors obtained for duplicate primary samples collected from the same truck- load of soil, but with different distances from the original primary sample. It is also discussed which contaminants are the major parameters responsible for final soil classification designations. The original primary samples for the soil classification were taken at the centre of a 7 × 7 m grid for every 33 cm to a depth of 1 m or 3 m. In addition - for approximately half of the positions - samples were taken at a distance of 1, 2 or 3 m from the original sample. The classifications for the original samples were compared to the classifications for the additional samples in the same Decision Unit (7 × 7 × 0.33 m box) and from duplicate original samples. The results show that at least 50 % of all samples were misclassified, 20 % were mis-classified by two or more classes. This study demonstrates that the risk of misclassification is highest for less mobile parameters, metals and PAHs compared to the volatile organic solvents.
Priorities and challenges in distance education

The 20th century constantly confronts new challenges for the entire society, especially for the education system. It offers teachers, parents, students, and the educational space as whole more opportunities for large-scale adoption of new technologies.The article discusses and substantiates that digital technologies are mediator tools for media consumption and that Georgia is successfully implementing them in practice.
Mission-Critical Public Safety Networking: An Intent-Driven Service Orchestration Perspective

Intent-based networking (IBN) provides a promising approach for managing networks and orchestrating services in beyond 5G (B5G) deployments using modern service-based architectures. Public safety (PS) services form the basis of keeping society functional, owing to the responsiveness and availability throughout the network. The provisioning of these services requires efficient and agile network management techniques with low-overhead and embedded intelligence. IBN incorporates the service subscribers in a model-driven approach to provision different user-centric services. However, it requires domain-specific and contextual processing of intents for abstracted management of network functions. This work proposes an intent definition for PS and mission critical (MC) services in beyond B5G networks, as well as a processing and orchestration architecture on top of MC push-to-talk (PTT) use case. The simulation results show that MC PTT services adhere to the key performance indicators of access time and mouth-to-ear latency bounded by approximately 250 and 150 milliseconds, respectively, with an additional overhead experienced during the intent processing in the range of 20- 40 milliseconds. This validates the premise of IBN in providing flexible and scalable management and service orchestration solution for PS next generation networks.
The Influence of Environment on Indonesian Language Learning

Language is a verbal communication tool used to interact with each other. Language will never be separated from the influence of the surrounding community.