Reviewerly/exHarmony huggingface dataset:
AAF-Net: Scene text detection based on attention aggregation features

With the advent of the era of artificial intelligence, text detection is widely used in the real world. In text detection, due to the limitation of the receptive field of the neural network, most existing scene text detection methods cannot accurately detect small target text instances in any direction, and the detection rate of mutually adhering text instances is low, which is prone to false detection. To tackle such difficulties, in this paper, we propose a new feature pyramid network for scene text detection, Cross-Scale Attention Aggregation Feature Pyramid Network (CSAA-FPN). Specifically, we use a Attention Aggregation Feature Module (AAFM) to enhance features, which not only solves the problem of weak features and small receptive fields extracted by lightweight networks but also better handles multi-scale information and accurately separate adjacent text instances. An attention module CBAM is introduced to focus on effective information so that the output feature layer has richer and more accurate information. Furthermore, we design an Adaptive Fusion Module (AFM), which weights the output features and pays attention to the pixel information to further refine the features. Experiments conducted on CTW1500, Total-Text, ICDAR2015, and MSRA-TD500 have demonstrated the superiority of this model.
Analisis Cloud Computing Untuk Penyimpanan Dokumen Terhadap Proses Pembelajaran Menggunakan Algoritma Regresi Linear Berganda (Studi Kasus : SMA Chandra Kusuma Jakarta Utara)

Obstacles in the learning process carried out by subject teachers and students of SMA Chandra Kusuma School, North Jakarta, because document storage is still low and internet access is still slow. Multiple linear regression algorithm to determine the relationship between cloud computing variables and the learning process on document storage variables. Simultaneously the F test results are 15.387 and the coefficient of determination is 36.30% so that there is a significant relationship between cloud computing and the learning process for document storage. Partially, cloud computing for document storage has a significant relationship because it has a t-value of 3.211 which is greater than t-table. And partially the learning process for document storage there is a significant relationship because it has a t-count value of 3.824 which is greater than t-table.
Perbandingan Metode Klasifikasi Data Mining Untuk Deteksi Keaslian Lowongan Pekerjaan di Medsos

The COVID-19 pandemic has resulted in more and more people losing their jobs. Due to layoffs or bankrupt companies. This has resulted in many people looking for job vacancies. Job vacancies are circulating on social media but there are real and fake ones. Irresponsible people create job vacancies on social media with fraudulent purposes or for personal gain. So, a comparison of data mining classification methods was made for the detection of authenticity of job vacancies on social media. The method used is naive bayes, KNN, and decision tree. In order to find out which method has the highest accuracy value and can be used to classify the authenticity of job vacancies, and fraud on social media can be prevented. Based on this research, the method that has the highest accuracy value is the KNN method. The accuracy value is 94.93%, while the Decision Tree model has an accuracy value of 91.57% and the Naive Bayes model has an accuracy of 84.35%. The KNN method is the best method for classifying the authenticity of job vacancies.
Requirements Analysis and Design of IRB System

Institutional review boards (IRB) are established by institutions to protect (human) research subjects from abuse.Researchers are thus required to apply and obtain IRB ap-proval for any research initiative that involves human subjects.Currently, most USA institutions use paper-based or partially automated approaches for IRB application, review, and approval processes.This can be cumbersome, error-prone and usually add unnecessary workloads to researchers.Moreover, paperbased approaches can delay or reduce research output, especially during collaborative research projects if researchers are from different institutions.Web-based information systems can be developed and used to streamline IRB application and approval processes.Before such web-based systems can be developed, the requirements of the system have to be analyzed and designed.However, requirements analysis and design are usually very tedious and expensive project, which may discourage some institutions from implementing systems to support IRB processes.The aim of this paper is to contribute a set of requirements and systems models that can be customized and re-used, by other institutions, to develop IRB Systems.Accordingly, we provide the following models, use case, feature tree model, and network diagram.In addition, we discussed our experiences and systems analysis principles we adopt to make this project a success.We believe that the experiences we share and the models we provide in this paper can inform practice and advance the knowledge of web-based information systems development.Re-using existing requirements and designs can also help to reduce the amount of time and cost required to develop similar systems.
MEMS Accelerometer Based Hand Gesture-Controlled Robot

Abstract: Gesture detection has gotten a lot of attention from a lot of different research communities, including humancomputer interaction and image processing. User interface technology has become increasingly significant as the number of human-machine interactions in our daily lives has increased. Gestures as intuitive expressions will substantially simplify the interaction process and allow humans to command computers and machines more intuitively. Robots can now be controlled by a remote control, a mobile phone, or a direct wired connection. When considering cost and required hardware, all of this adds to the complexity, particularly for low-level applications. MEMS accelerometer-based gesture-controlled robot attempts to create a gesture-controlled robot that can be commanded wirelessly. User is able to control motions of the robot by wearing the controller glove and performing predefined gestures.
Development of Course Booking System (Front-end &amp; Back-end) Based on Mobile Application to Help Tutoring Elementary and Junior High School in Indonesia

Tutoring is informal education to help students who have problems outside of school. Students are helped to adjust to their learning situation so that they can develop efficiently according to their abilities. In Indonesia, there are still many students who do not understand the learning material delivered at school, so they must deepen the material outside of school learning. Especially since the COVID-19 pandemic hit, the effectiveness of learning activities has decreased because the learning process is carried out online which results in less interactive learning. Therefore, the EduAlecta application was created which is engaged in digital-based education. The EduAlecta app can help teachers increase their income and connect with students who need tutoring outside of school hours. The EduAlecta application system has the advantage of applying GPS so that students can determine the study location they want and students can arrange the tutoring they ordered. The method used in the front-end design is user centered design and the back-end design method uses waterfall.
The Implementation of Augmented Reality in Keraton Kanoman’s Museum

Museums genereally display historical objects such as heirlooms and artifacts. To observe this object more clearly visitors usually touch the display case or even hold the objects directly. The implementation of new normal expected that people avoiding contact either directly and indirectly. This study aims to find an alternative solution for reducing contact in museum, This study aims to implement the Augmented Reality (AR) technologies as one of solution for reducing contact in museums, by implementing it in form of mobile application that using Android operating system supported by Unity AR Foundation and ARCore. This research conducted at Keraton Kanoman in Cirebon City. The results of performances test showed 4 out of 10 objects displayed and running 60 Hz Refresh Rates and 60 fps. While the AR/VR development in Android is still limited due to ARCore incompatibility issue, the device proofed to run smoothly without having significant Refresh or Frame Rates drop
A Critical Account of Articulatory Phonology: A Subject Review

Phonological theories are in constant search for common points among natural languages, but there are complexities in applications weaking such theories. Articulatory phonology started as a system of gestures that are based on phonological units depending on phonetic articulation. However, it has ended widith loose terminology, a poor economy of principles and rules, vague concepts and dimensions, following subjective methods of analysis, and poor workability. This has left the theory without further applications and studies that are yet to be addressed.
Integrated representation for discrete Fourier and wavelet transforms using vector notation

Many mathematical operations are implemented easily through transform domain operations. Multiple transform domain operations are used independently in large and complex applications. There is a need to develop integrated representations for multiple transform domain operations. This paper presents an integrated mathematical representation for the discrete Fourier transformation and the discrete wavelet transformation. The proposed combined representation utilizes the powerful vector notation. A mathematical operator, called the star operator, is formulated that merges coefficients from different transform domains. The star operator implements both convolution and correlation processes in a weighted fashion to compute the aggregated representation. The application of the proposed mathematical formulation is demonstrated successfully through merging transform domain representations of time-domain and image-domain representations. Heart sound signals and magnetic resonance images are used to describe transform-domain data merging applications. The significance of the proposed technique is demonstrated through merging time-domain and image-domain representations in a single- stage that may be implemented as the primary processing engine inside a typical digital image processing and analysis system.
Image matching via progressive priors

It is a fundamental and challenging issue how to improve the accuracy of image matching in computer vision. To address this issue, an image matching method is proposed, which is via progressive priors of a putative dataset. Distance ratio antecedents of a presumptive dataset are initially employed to calculate a tentative deformation through geometric constraints. Progressive priors of the presumptive dataset, obtained by the tentative deformation, are then engaged to improve the accuracy of image matching by estimating a global deformation. The comparison experiments illustrate that our proposed method more effectively enhances the accuracy of image matching than six state-of-the-art methods.
The Effect of Monetary Policy on Economic Growth in Ethiopia

There is an ambiguous relationship between monetary policy and economic growth both in theoretically and empirically. Hence, this study was examined the link among monetary policy and economic growth in Ethiopia by using time series data from 1980-2019. Zivot and Andrews (ZA hereafter) and Clemente (CMR hereafter) unit root test revealed that real interest rate was stationary at level. while log of real gross domestic product, log of gross capital formation, log of money supply, exchange rate, reserve requirement and consumer price index were stationary after first differencing in both tests. The results of ARDL bounds test for co-integration showed the present of long run relationship among the series during the study period. Long run estimation indicated that the existence of positive statistical significant effect of money supply, real interest rate, reserve requirement and gross capital formation on economic growth in Ethiopia during the study period. While, exchange rate has negative effect on economic growth. The study also revealed that, inflation has no significant effect on economic growth in the long run. The short run estimation showed that except inflation index, other variables (money supply, gross capital formation, reserve requirement and real interest rate) has positive significant effect on economic growth in Ethiopia in the study period. However, exchange rate has no significant effect in the short run. The Error correction model (ECM) test showed that about 29% of short run disequilibrium was be adjusted every year. The test of causality showed that a causation between real gross domestic product with inflation, exchange rate and money supply at 5% level of significant and a causality with gross capital formation at 10% level of significant. In conclusion, there were positive relationship most of the monetary policy instrument variables with economic growth except inflation and exchange rate for this study. As a recommendation, national banks of Ethiopia should be committed to the mission of price stability.
Evaluation of Antibiotic Resistance Pattern of Bacteria Obtained from Surfaces of Automated Teller Machines in Calabar South, Cross River State, Nigeria

Money is an essential commodity in the life of humans and it is required to meet daily needs; as such, there is always a consistent usage of automated teller machines (ATM) to withdraw money to meet these needs.In this process, however, there is exchange of pathogenic and non-pathogenic microbes resulting from direct contact from the numerous users of these piece of equipment, especially with hands which are even known to be a 'house' of microorganisms.The study was carried out to evaluate the antibiotic susceptibility patterns of bacteria present on Automated Teller Machines (ATM) in Calabar Communities in Calabar, Cross River State, Nigeria.One hundred and fifty (150) samples were collected and analysed using standard microbiological procedures.Isolates were identified by microscopic, biochemical and carbohydrate fermentation characterizations.The susceptibility of the isolates to antibiotics was determined by the modified Kirby-Bauer disc diffusion method.Results revealed that all ATMs harbored seven different species of bacteria namely Escherichia coli, Staphylococcus aureus, Bacillus subtilis, Pseudomonas aeruginosa, Salmonella typhimurium, Klebsiella spp., and Shigella spp.However, Staphylococcus aureus was the most frequently occurring bacterial isolate (28.3%) followed by Escherichia coli (18.6%),Bacillus spp.(17.7%),Pseudomonas aeruginosa (14.2%),Salmonella typhimurium.(9.7%), Klebsiella spp.(6.2%) and Shigella spp.(5.2%) respectively.Antibiotic susceptibility results showed a zone of inhibition ranging from 8mm to 45mm.The widest zone of inhibition (45mm) was recorded with Augmentin for Salmonella typhimurium while the least zone of inhibition (8mm) was recorded in Ampicillin for Pseudomonas aeruginosa.All bacterial isolates showed a high level of resistance (6mm) to Nalidixic acid except Klebsiella spp.(10mm) and Salmonella typhimurium.Overall, Ceproflox and Quinolone showed the highest efficacy against the bacteria isolates followed by Augmentin and Tarivid.Heavy bacterial presence on ATM surfaces is evident from this study.Frequent disinfection of ATMs surfaces and their accessories along with periodic microbiological surveillance is thereby recommended.
Retraction of published article due to the author's proposal, Vol. 2021, Iss. 3, pp. 375-387

The editorial board of Educational Technology Quarterly, announces the formal Retraction of the following article: Pursky, O., Selivanova, A., Buchatska, I., Dubovyk, T., Tomashevska, T. and Danylchuk, H., 2021. Features of learning motivation in the conditions of coronavirus pandemic (COVID-19). Educational Technology Quarterly, 2021(3), pp. 375-387. Available from: https://doi.org/10.55056/etq.31
Research on automatic recognition method of basketball shooting action based on background subtraction method

In order to overcome the problems existing in the existing methods, such as high rate of false recognition, high rate of missing recognition and low rate of recognition, an automatic recognition method of basketball shooting action based on background subtraction method is proposed. The background information of the target image is obtained by the background subtraction method to improve the clarity of the whole contour of the moving object. A certain number of video frames are extracted, and the background in the image is effectively separated. The two-dimensional Fourier transform is used to balance the video frames to obtain the foreground target information and the background of the video sequence, so as to complete the automatic recognition of basketball shooting action. Experimental results show that the proposed method can effectively reduce the rate of false recognition and missed recognition, and improve the recognition rate.
The concept of improving the security of IT systems supporting the storage of knowledge in organizations

This article presents the concept of improving the security of information systems intended for storing knowledge in organizations. It is designed to protect knowledge from leakage, theft or destruction, but without denying access to its resources to employees. It is possible thanks to the introduction of the so-called security and sharing groups (S&S groups) that have two attributes at the same time – the level of knowledge security and the level of knowledge sharing. The proposed concept also assumes assigning knowledge categories to data and enabling the organization to manage the allocation of operations on data from individual knowledge categories to S&S groups. As a result, the system enables the protection of the most valuable knowledge resources, without restricting access to its other categories. The developed concept makes it easier to find a compromise between the strength of security and the free flow of knowledge at the time of system implementation. In addition, the dynamic ability to choose the level of security and the level of access to knowledge in the system allows adaptation to changes taking place in the environment of the organization and increases its resilience.
CNN based System for Road, Vehicle Detection and Segmentation from Aerial Images

Image segmentation is crucial for computer vision. Visual segmentation simplifies image analysis. Detecting and dividing highways is a major difficulty in aerial traffic monitoring, autonomous driving, and border surveillance. This is tough. Image segmentation traditional algorithms are unsuccessful, according to the literature. Segmentation of the semantic field divides an image into semantically relevant components and assigns each to a class. Deep convolutional neural networks accurately segregate semantics. In deep learning, a convolutional neural network utilizes an input image to rate the relevance of various things. This work used convolutional neural networks to recognize and segment roads in aerial photos. SegNet and DeepLabv3+ use Vgg16 and ResNet-18 pre-trained models for road recognition and segmentation from aerial photos. SegNet based on Vgg16 produced high accuracy, whereas DeepLabv3+ based on Resnet-18 was efficient in terms of accuracy and training time. The suggested system designs use MATLAB. Learning and operational phases are included in the algorithm for image segmentation. In MATLAB, convolutional neural network analyses tagged aerial photographs. This work trains a convolutional neural network to accurately extract road features from aerial images. This system identifies roads and automobiles quickly for traffic monitoring.
Real-time Transformer Vandalism Detection by Application of Tuned Hyper Parameter Deep Learning Model

Vandalism is an illegal act of cannibalism or change of face to a private or public property by human beings for re-sale of parts or to punish the property owner. Initial research findings on transformer Vandalism detection have fallen short of human image recognition of the vandal in real-time but only does detection of activities after the damage is done or as it occurs. Automated real-time systems using sensor feed to a trained deep learning model is a new transformer vandalism detection approach with capabilities of three-dimensional image learning, extracting important image features automatically and temporal output prediction. This paper aims at distinguishing the human object entering a zoned transformer area without permission to take away or modify the established infrastructure, so that the Vandal can be arrested before causing any damage to the transformer. The researchers identified a multiplicative hybrid model combining convolutional neural networks and long short-term memory for application to vandalism problem to detect the image of a vandal as it enters a restricted transformer installation site. The image recognition accuracy can be improved by tuning the model hyper-parameters and the specific hyper-parameters considered in this research work are number of model layers and epochs. The human object is distinguishing by applying the image features taken with Image sensor to a trained deep learning model. The hybrid deep learning method increases the output prediction accuracy from the input data and lowers computational processing complications due to a reduced data volume through pooling. The system is trained and validated using ImageNet dataset. Results achieved by five layers and sixty epochs is 99% recognition accuracy. The performance of the system with an increased number of layers and epochs to five and sixty respectively was the best result as compared with lower layers and epochs. Further increase of these parameters resulted to system overfitting.
Recent developments in information extraction approaches from Arabic tweets on social networking sites

Information extraction from Arabic tweets has attracted the attention of researchers due to the huge data accessibility for the swift expansion of social media platforms. With the increasing use of social web applications, information extraction from the various platforms has gained importance for understanding the trending post and events predictions based on those sentiments written by the users on certain news feeds. The Arabic Language is mostly used in Middle Eastern and African countries and most users tweet on social media using the Arabic language, therefore Arabic text classification and sentiment analysis aimed to predict information extraction from social media platforms. This research provides a more detailed critical review of the information extraction presented in the literature focused on using different tools, methods, and techniques like k-NN, support vector machines, Naïve Bayes, and other machine learning tools for the data extraction and processing.