Reviewerly/exHarmony huggingface dataset:
Deteksi Atap Bangunan Berbasis Citra Udara Menggunakan Google Colab dan Algoritma Deep Learning YOLOv7

This article presents a deep learning-based approach for roof building detection in aerial imagery using the Yolov7 algorithm and Google Colab. We trained the Yolov7 model on a dataset of annotated aerial images and tested its performance on a testing set of 200 images. Our experimental results show that the Yolov7 model achieved an F1 score of 80% for roof building detection on the testing set, indicating that our approach can accurately detect roof buildings in aerial imagery. This approach can be useful in various applications, such as urban planning, disaster management, and infrastructure development. The availability of aerial imagery and the ability to use deep learning algorithms make it possible to automate the process of roof building detection, which can save time and resources. This research can be extended by investigating the use of other deep learning algorithms or combining multiple algorithms to improve the accuracy of roof building detection in aerial imagery.
Exploring academics’ perspectives related to the adoption of Augmented Reality applications within an e-learning environment in Higher Education institutions: The role of AR self-efficacy, innovation resistance, perceived AR fatigue and technology involve

The learning environments is in continuous change and relies heavily on new technologies. Augmented Reality (AR) applications and tools are a novel e-learning tools that could be an essential component to the educational process and can accelerate students’ learning in virtual classrooms. Augmented reality applications focus on the student's involvement in the learning process and try to close the gap between student's capabilities and the real-life experience. This paper intends to explore university academics' perspectives and acceptance to use Augmented Reality applications within an e-learning environment in Higher Education institutions. A conceptual model was developed based on Theory of Planned Behaviour (TPB), Innovation Resistance Theory (IRT), Innovation Diffusion Theory (IDT) and Social Cognitive Theory (SCT). PLS-SEM was used to analyse data from 275 university academics in two large universities in Saudi Arabia. The study findings showed that subjective norm, AR self-efficacy (SE), perceived AR fatigue, technology innovativeness and innovation resistance significantly influenced the intention to use AR as e-learning tool. Moreover, complexity showed insignificant results. The implications of this study are discussed and proposals for future research initiative are presented.
Meningkatkan Kompetensi Guru Memotivasi Siswa Melalui Running Dictation Strategi Pelajaran Bahasa Inggris

Learning at SMA Negeri 1 Painan, especially the problem found in language learning was that the teacher was still weak in motivating students during PBM. This is evidenced by the fact that there are still many students who do not focus when the teaching and learning process takes place. To overcome this problem the authors conducted school action research to improve teacher competence to find the right strategy in motivating student learning. This research is entitled to improve teacher competence in motivating student learning through running dictation strategies in language lessons at SMA 1 Lunang. The purpose of this study was to see how far there was an increase in students' learning motivation and students' enjoyment of learning a language. The action is carried out in two cycles. The data were obtained from observation sheets recorded by an observer who recorded student activities observed during the learning process. Based on the research results, the percentage of students increased from cycle I to cycle II, from 65.7% to 86%. Compared to the average percentage before the study it was only 54.4 as many as 11.6% of students. It can be concluded that learning by creating a fun learning atmosphere through running dictation strategies can increase student motivation.
TraViQuA: Natural Language Driven Traffic Video Querying Using Deep Learning

Video cameras are widely utilized and have ingrained themselves into many aspects of our daily life.Analysis of video contents is more challenging as the size of the data collected from the cameras increases.The fundamental cause of this challenge is because certain data, like the videos, cannot be queried.Our research focuses on converting traffic videos into a structure that can be queried.Specifically, an application called TraViQuA was suggested f or natural language-based car search and localization in traffic videos.To query and identify cars, data including color, brand, and appearance time are used as features.The query is initiated in real time on live traffic feed, as the user enters the search term on the application interface.Our text to SQL conversion algorithm enables the mapping of a search term into a SQL query.Based on the response to the natural language query, TraViQuA can start the video from the relevant time.Deep neural networks were employed in our application for text to SQL conversion and feature extraction.Our research reveals that color and brand models had mean average precision of 98.714% and 91.742%, respectively.The text to SQL conversion had an 80% accuracy rate.To the best of our knowledge, TraViQuA is the first application that enables police officers to input a natural language description of a car and discover the car of interest that matches the description, bridging the gap in traffic video surveillance.Moreover, TraViQuA can be incorporated into other intelligent transportation systems to support law enforcement officials in urgent situations like hit-and-run incidents and amber alerts.
Deep Learning-Based Intelligent Image Recognition and Its Applications in Financial Technology Services

The financial technology service industry involves a large number of image and text information processing tasks.By automatically processing images and text information, financial institutions can greatly reduce labor costs, improve overall operational efficiency, and help financial institutions identify and predict risks more accurately, thereby improving risk management capabilities.The existing image symbol recognition and scene text detection methods may be affected in terms of recognition accuracy when processing complex scenes, low-resolution images or texts affected by obstacles, distortions and other factors.To this end, this study conducts an in-depth study on the application of deep learning-based intelligent image recognition in financial technology services.It elaborates the application scenarios of image symbol recognition and scene text detection in financial technology services.The ASTER model is improved, and the combination of attention mechanism sequential decoding can effectively capture local information and global dependencies in the feature sequence, thereby improving the recognition accuracy of the image symbol recognition model.By focusing on the center point position information of the text, pixels with the same center point are aggregated to reduce the interference between adjacent texts to some extent, achieving more accurate text segmentation.Experimental results validate the effectiveness of the method in this study.
Human Face and Facial Expression Recognition Using Deep Learning and SNet Architecture Integrated with BottleNeck Attention Module

Thermal infrared face image recognition with the help of deep learning technology has become the most debated concept in research area nowadays.Many articles are done and being working on this area to discover novel findings.Thermal infrared images can be recognised irrespective of light conditions, aging and facial disguises.This paper proposes a method named SNet integrated with BottleNeck Attention Module (SN-BNAM) for thermal face image recognition using SENet architecture in which the BottleNeck Attention Module is integrated.After squeeze and excitation process, the channel and spatial attention is inferred as two separate branches inside the BottleNeck Attention Module (BAM).This module is placed at each BottleNeck area.The SN-BNAM module can be integrated with any feed forward convolutional neural networks.The efficiency of the proposed system is evaluated by experimenting on various architectures and object validation is done on VOC 2007, MS COCO, CIFAR-100 and ImageNet-1K datasets.These experiments proves that our method shows consistent improvement in image classification and object detection.
Master-Slave Convolutional Deep Architecture for Vehicle Identification and Type Classification

Video surveillance of road traffic plays an important role in highway safety and is an important application of intelligent transportation systems.One of the basic applications of intelligent transport systems is the detection and classification of vehicle types.The major problems encountered by these systems are the significant similarity between the vehicles, frequent occlusions on the highway, and low resolution of the surveillance cameras.This paper proposes a novel convolutional neural network architecture called master-slave convolutional deep architecture for vehicle detection and type classification.The basic concept of this architecture is twofold: a.The sequential operation of the two networks where the slave network only works if the master network detects vehicles in the road scene will allow a considerable reduction in the search area for vehicles.It will induce a significant reduction in processing time.b.A combination of deep-shallow neural networks allows the system to share the knowledge gained from the vehicles on two networks.The first (master) shallow learns the shape of vehicles while the second (slave) is responsible for learning all the details of vehicles to distinguish the different classes.The experimental results, performed on 3200 images, have shown that the favorable performance of the proposed CNN architecture allowed us to achieve successful detection with TPR of 92% and TNR of 95% and vehicle type classification with a considerable mean average precision of 93.38% where cars classification gives the highest rate (98.63%).
The Learning Achievement of Airline Business Students on the Virtual Experience during the Pre-flight Emergency Equipment Check Procedures on the Airbus A320 Series Aircraft

The emergence of disruptive technology in mid-1999 caused the industrial revolution, which leverages the benefits of the industry’s products and elaborates new ideas for service sectors to a new level. The recent virtual technology brings opportunities to higher education along with these digital adaptations. However, there is a need for experimental research exploring the educational influence of using immersive virtual reality in aviation safety subjects. This quasi-experimental aimed to 1) address the Airline Business students’ learning achievement after using virtual reality technology due to retaining knowledge influenced by virtual experience. 2) study the capability of immersive technology based on the Airbus A320 series aircraft’s virtual environment that establishes the Airline Business students’ learning development in three areas: cognitive domain, psychomotor domain, and affective domain. Two groups of 30 participants were recruited based on a simple random sampling from a total number of 75 third-year Airline Business students in the experimental and control groups. The parametric statistics were conducted on a hypothesis testing by comparing two measurements from the experimental and control groups. The results revealed that the test scores of the experimental group obtained significantly better academic achievement in terms of the knowledge needed to perform tasks than the control group. Besides, the behavioral learning and emotional intelligence required to complete performance-related tasks were noticed in the experimental group. In addition, the effectiveness index (EI) was computed to affirm that the innovative learning tool can be an integral part of the future physical classroom. It indicated that students effectively learn in the aviation safety context via practicing through virtual reality. The virtual experience positively helps motivate and engage students in an aviation safety study. It also profoundly enhances the Airline Business students’ competency, including knowledge, skills, and attitudes needed to perform their future responsibilities as flight attendants.
AN ERROR ANALYSIS OF USING SIMPLE PRESENT TENSE IN WRITING DESCRIPTIVE TEXT WRITTEN BY STUDENTS OF THE TENTH GRADE AT SMA N 1 GODONG

This final project studied the analysis of the errors made by the students. The background of the study errors made the students. This research focused mainly on the errors finding. The design of the research was descriptive qualitative research. The objectives of the study were (1) to find out the kinds of errors finding, (2) to explore dominant errors findings, (3) to show the students competence producing descriptive text. The object of the research were tenth grade SMA N 1 Gondong. The instruments of the study were the data from the students product. To identify the data of error analysis the researcher used the theory of James Carl (1998). This finding showed us how the errors finding. The dominant types of errors analysis is the omission 42 findings with 42,42% percentage finding from the data. Then, followed by the misinformation that found 32 errors with 32,32%. The 15 errors of addition with the percentage 15,15% findings from the data. The last one is misordering with 10 errors with 10,10%. The summaries may be concluded that there were several finding on the errors analysis. There were omission, addition, misinformation, and misordering.
The Use of Clustering Technique to Improve Students’ Writing Skill in Descriptive Text (A Case of The Seventh Grade Students of SMP Negeri 1 Kragan Rembang)

AbstractThis research focuses on improving writing skills in descriptive text using clustering technique. Writing skill is one of the skills that are quite complicated to learn because it requires extra thought to produce words, phrases, sentences, and even paragraphs in a text. Many students still find difficult to explore and develop their idea, students write content not related to the topic, and students write ideas and sentences are not organized well. The sample was students of the seventh-grade of SMP Negeri 1 Kragan Rembang with total was 64 students. The researcher uses experimental research. There were two groups namely experimental group consist of 32 students and control group consist of 32 students. Instrument of the research was writing test. The researcher conducted pre-test and post-test sessions. Data collection was done by using quantitative method. Based on data analysis from the experimental group showed that the mean of pre-test was 58.84 while post-test was 72.84. In the control group showed that the mean of pre-test was 60.59 while post-test was 68.13. In the T-test showed that the sig. (2-tailed) of experiment and control group were 0.000 &lt;0.05. It means that there is a significance difference in the result of students' learning scores between experiment and control group. The result of this research showed that clustering technique can improve students’ writing skill in descriptive text. Clustering technique is a good and easy technique to help develop and organize ideas in writing.
FINANCIAL DIGITALIZATION AND ECONOMIC GROWTH: RECENT DEVELOPMENTS IN BANKS AND FINTECH

High technology enables payment evolution and global competition. The ambiguity surrounding the digital currency still leaves ample room for analysis of its unreserved acceptance, trust and anticipation, which are the main drivers of the network's spread. Bitcoin and other cryptocurrencies have the potential to replace traditional and new forms of payment. But to achieve this and become a dominant force in the global payments system, they must provide distinctive individual value, address and overcome a number of critical challenges, such as formal regulatory issues. Banks should take a close look at the technology underlying these cryptocurrencies as a potential generic new way to transfer ownership of value in the long term. The objective view is that the future of digital currency is currently still an open question due to the existence of a "critical mass". The research showed that the future of cryptocurrencies can be bright if some institutional-formal conditions are met due to the fact that the successful evolution of e-money requires building security payments through three criteria – standardization, compatibility and innovation. Also, the partnership between Fintech companies and banks is an already implemented formula that bears fruit, but we must not leave out the fact that all developed countries in their platforms, in addition to connecting banks, implement the platforms with the direct participation of e-Government and the concept of interoperability.
Particle Swarm Optimization for Web Document Retrieval Based on Term-Document Matrix

Retrieving relevant information from the enormous volume of data is the top most priority of information retrieval systems. As technology arises, various search engines help us to track the relevant data and information on the user’s needs, but the amount of information available is huge and complex. Sometimes, user finds it difficult to find the data according to their need. When people provide the same queries, the same set of relevant documents will be returned by classical search engines. It gets hard for the users to retrieve the relevant documents. To solve this problem, a developed document search technique has been proposed, which is based on Particle Swarm Optimization. It helps to optimize web document information retrieval. PSO algorithm is easy to use and can have better results as it is a faster method compared to other methods. PSO algorithm is being applied on Term-Document Matrix which consists of weights calculated by TF-IDF weighting formula. Term-Document Matrix is the search space on which the PSO is applied by considering the query as a particle and the position of the particles is initialized randomly. This method is tested experimentally on a big TREC 2019 dataset. By applying PSO, the results are compared with the results obtained through traditional approaches.
The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21

First posted May 23, 2023 For additional information, contact: Director, Fort Collins Science CenterU.S. Geological Survey2150 Centre Ave., Bldg. CFort Collins, CO 80526-8118 The U.S. Geological Survey Science Application for Risk Reduction (SAFRR) Project has created four major hazard scenarios—ShakeOut, ARkStorm, Tsunami Scenario, and HayWired—with multidisciplinary teams of scientists, academics, and practitioners. By presenting a clear and highly detailed narrative of potential damage from earthquakes, tsunamis, and winter storms, the scenarios are intended to foster science-based preparedness strategies and disaster risk reduction innovations.This evaluation explores the presence of these scenarios in cultures of preparedness and their role in disaster risk reduction, and reports barriers and enablers to creating and using these scenarios. To do this, the evaluation team developed a mixed-methods study that includes background research for each scenario, qualitative interviews, data collection of media and academic engagement, and examples of SAFRR scenario use in hazard planning. The data collection led to the development of a hazard scenario evaluation tool that combines theories from multiple disciplines to create a best practice set of categories that aid in scenario use and efficacy. The evaluation tool categories—actionable, longitudinal, educational, relevant, and thorough—are organized as a series of checklists that are used to determine how the scenario planners prioritized different aspects of the scenarios to achieve their goals. The tool could also be used to aid scenario planning for other regional disaster risk reduction scenarios of a similar scope.Findings from this evaluation include detailed narratives of scenario use over time, demonstrating that the scenarios have continued to be useful in hazard planning and preparedness across the globe. Examples of use include using the scenarios to advocate for resilient building and development policy, to promote hazard response exercises, and as source data for the development of new hazard models and science. The scenarios themselves are innovative, both in the hazard science created for scenario development and in their branding and public engagement as U.S. Geological Survey products. This SAFFR retrospective is a descriptive evaluation and does not formally address the effects of the scenarios. Nevertheless, this report does include evidence of scenario affects as discovered through qualitative interviews and research, which is presented to explore how the SAFRR scenarios have been received by cultures of preparedness.
Analysis of the Impact of Covid-19 on the Educational Process

The Covid-19 pandemic has created serious problems for higher education around the world. The created situation led to a number of challenges, which were reflected quite differently on regions, countries and institutions, and further aggravated the existing inequality. The article describes various forms of the impact of Covid-19 on the situation created in higher education institutions and the mechanisms for overcoming the created challenges. The results of surveys conducted by the International Association of Universities in many universities of the world in 2020-2022 are presented. The results of the studies conducted in the direction of higher education management during the pandemic and the changes experienced by the education finance sector are analyzed. The problems of introducing new methods and transitioning to distance learning in the field of learning and teaching and ways to solve them are discussed. The trends formed by the impact of the pandemic from the point of view of scientific research in universities, the analysis of the positive and negative effects of the pandemic on research activities are shown. Both global and regional data on the involvement of university students and professors in public-social processes are provided. The results of the first two years of the impact of the pandemic on higher education are summarized. Appropriate conclusions have been made about the impact of the Covid-19 pandemic on the full-fledged work of higher educational institutions.
Educational Record Authentication Using Decentralized Consortium Blockchain Technology

Abstract: The current facilities provided by educational institutes across the global to authenticate educational records use centralized approach of maintaining a server. This introduces two key challenges. One is to provide sharing and browsing facilities even when there is server maintenance. The other is to prove the authenticity of the records. The first challenge may be resolved using cloud-based techniques. To address the second challenge, we propose a consortium blockchain based solution. By using off-chain storage for record update and management, third party modification to any valuable data can be known. The nodes in the decentralized peer-to-peer network are servers handled by recognized educational institutes which may be autonomous or affiliated. Using cryptography, the hash information of records is stored on a decentralized blockchain. This enables a student or a third party to quickly verify his/her marks by verifying hashes of them from a reputed peer-to-peer network