Reviewerly/exHarmony huggingface dataset:
A Novel Hybrid DL Model for Printed Arabic Word Recognition based on GAN

The recognition of printed Arabic words remains an open area for research since Arabic is among the most complex languages.Prior research has shown that few efforts have been made to develop models of accurate Arabic recognition, as most of these models have faced the increasing complexity of the performance and lack of benchmark Arabic datasets.Meanwhile, Deep learning models, such as Convolutional Neural Networks (CNNs), have been shown to be beneficial in reducing the error rate and enhancing accuracy in Arabic character recognition systems.The reliability of these models increases with the depth of layers.Still, the essential condition for more layers is an extensive amount of data.Since CNN generates features by analysing large amounts of data, its performance is directly proportional to the volume of data, as DL models are considered data-hungry algorithms.Nevertheless, this technique suffers from poor generalisation ability and overfitting issues, which affect the Arabic recognition models' accuracy.These issues are due to the limited availability of Arabic databases in terms of accessibility and size, which led to a central problem facing the Arabic language nowadays.Therefore, the Arabic character recognition models still have gaps that need to be bridged.The Deep Learning techniques are also to be improved to increase the accuracy by manipulating the strength of technique in a neural network for handling the lack of datasets and the generalisation ability of the neural network in model building.To solve these problems, this study proposes a hybrid model for Arabic word recognition by adapting a deep convolutional neural network (DCNN) to work as a classifier based on a generative adversarial network (GAN) work as a data augmentation technique to develop a robust hybrid model for improving the accuracy and generalisation ability.Each proposed model is separately evaluated and compared with other state-ofthe-art models.These models are tested on the Arabic printed text image dataset (APTI).The proposed hybrid deep learning model shows excellent performance regarding the accuracy, with a score of 99.76% compared to 94.81% for the proposed DCNN model on the APTI dataset.The proposed model indicates highly competitive performance and enhanced accuracy compared to the existing state-of-the-art Arabic printed word recognition models.The results demonstrate that the generalisation of networks and the handling of overfitting have also improved.This study output is comparable to other competitive models and contributes an enhanced Arabic recognition model to the body of knowledge.
Geochemical Speciation and Principal Component Analysis of Toxic Metals in River Ogun Coastal Sediments, Southwestern Nigeria

This study investigated the influence and interactions of geochemical fractions of toxic metals in sediments of River Ogun in Nigeria. Fe, Mn, Zn, Pb, Cr, and Co were significantly present across the area, while Cu, Ni, and Cd were negligible. All the metals, except Zn, were found highly above their permissible limits in sediments. The bulk metal concentrations followed the trend: Fe &gt;&gt; Mn &gt; Zn &gt; Pb &gt; Co &gt;&gt; Cu ? Cr ?Ni &gt;&gt; Cd. At p = 0.05, the concentrations (ppm) of Ni, Zn, Co, Mn, Fe, Pb, Cr, Cd, and Cu in the F1 were 1.72–5.84, 13.2–15.0, 1.16–3.41, 56.1–77.2, 198–212, 9.32–14.3, 1.96–3.66, 0.55–1.61, and 0.56–1.98, respectively, while the values were 12.1–13.6, 113–270, 67.8–152, 206–275, 514–637, 131–179, 18.5–27.0, 1.31–1.75, and 6.11–8.11 for the F5, respectively. Overall, Cd and Fe showed the least and highest distributions in the various geochemical forms, respectively. Moreover, the abundance and mobility of Mn, Ni, Fe, Cr, Cd, and Zn correlated significantly in exchangeable and carbonate-bond fractions. Hence, environmental monitoring of the sediments and Pisces should be carried out while remediation of the location is recommended.
Research on low impact landscape design in urban rainwater utilization technology environment

In order to further explore how to design a low-impact landscape under the technical environment of urban rainwater utilization, this study takes the low-impact landscape design of a certain place as an actual case, makes a quantitative analysis of the actual situation of the area based on GIS and other technologies, then formulates a low-impact landscape design scheme according to the analysis results, and finally analyzes the overall effect of the scheme. The analysis results show that the design scheme has more obvious benefits under the condition of medium and small precipitation, which can provide some reference for future related work.
Pollution Status of Dumpsites within Uyo Metropolis, Akwa Ibom State, Nigeria

The study evaluates the heavy metals, geo-accumulation index and contamination factor in soil around dumpsites in Uyo, Akwa Ibom State, Nigeria. Soil samples were collected, during rainy season, at depth of 0-15cm and 15-30cm from two study sites (A, B) and a control site (C). Soil samples were all analyzed for heavy metals (Pb, Ni, Cd and Cu) using Atomic Absorption Spectroscopy (AAS). The concentration of Lead at site A for depth 0-15 cm ranged from 13.71 -14.90 mg/kg with a mean value of 14.38 ± 0.61 mg/kg; Nickel varied from 2.35 – 3.22 mg/kg (2.74 ± 0.44 mg/kg); Cadmium ranged from 3.78 – 4.13 mg/kg (3.93 ± 1.18 mg/kg); Copper ranged from 27.84 – 28.34 mg/kg (28.14 ± 0.27 mg/kg). The heavy metals concentration in soil at sites A and B followed the sequence, Cu &gt; Pb &gt; Cd &gt; Ni while at the control site; Cu &gt; Cd &gt; Ni &gt; Pb. The calculated geo-accumulation index (Igeo) showed that the soil samples from sites A and B for Pb, Ni and Cu belong to class 2, which implies that the soil was moderately contaminated. The Igeo values for Cd at sites A and B were classified as 4, indicating a strongly contaminated soil. However, Igeo of Cd at the control site is class 3, implying moderately to strongly contaminated soil. Calculated contamination factor (CF) showed that Pb, Ni and Cu for sites A, B and control at depths 0-15cm were in class 1 (low contamination), while Cd contamination was class 4 except at control site (15 -30cm) which showed CF of class 3. The contamination degrees (CD) for the sampling sites A and B showed considerable contamination and their pollution load index (PLI) indicated a progressive deterioration of the sites; however, the control site recorded very minimal state of pollution. Generally, the results found that the top soil (0 – 15 cm) was more contaminated than the sub soil (15 – 30 cm) of all sample sites studied. The results of this study imply that the inhabitants around Sites A and B are exposed to Cadmium pollution and the soil is not suitable for agricultural practice, since plants can take up these toxic metals and bio-accumulate them in their tissues. Cadmium, at high concentration in plants, can cause serious clinical and physiological effects on humans when consumed as food.
Analysis of Scientific Educational Game Tools Implementation for Early Childhood Students in West Nusa Tenggara

This qualitative descriptive study aims to determine the implementation of scientific educational game tools for early childhood in West Nusa Tenggara. Samples were taken using a simple random method from 3 districts in West Nusa Tenggara, which are North Lombok, East Lombok, and West Sumbawa. Data were collected using a questionnaire form. The research data were analyzed using a qualitative descriptive method. The results of this research illustrate the implementation of scientific educational game tools for early childhood in the West Nusa Tenggara province. Based on the data obtained, as many as 14 scientific educational game tools have been found and are suitable for science learning in early childhood. There are 11 types of indoor scientific educational game tools that can be used to stimulate science learning in early childhood and as many as 3 types of outdoor scientific educational game tools that can be used to stimulate science learning in early childhood. The implementation of scientific educational game tools that are in accordance with the science concept is only 28% and was categorized as low. This shows that the teacher's understanding and competency in teaching science with the help of scientific educational game tools is still limited
COMPARISON OF LEUKOCYTE COUNTING METHODS OF HEMATOLOGIANALYZER AND HEMOCYTOMETER IN TOTO HOSPITAL KABILA BONEBOLANGO

Examination of leukocyte count is a routine blood test that is widely requested in health care units, both clinics, health centers or even in hospitals. This study aims to establish the diagnosis of the disease. One laboratory examination, namely a hematological examination, to find out the comparison of leukocyte counts with the Hematology Analyzer and Hemocytometer methods at Toto Kabila Hospital, Bone Bolango Regency.&#x0D; The type of research used is Observational Analytic Research with a quantitative approach, analytical research is research that looks for relationships between variables, namely by analyzing comparisons. The sample size taken was 30 samples with the sampling technique using purposive sampling. This type of research wanted to see the results of a comparison of leukocyte counts using the hematology analyzer and hemocytometer methods at Toto Kabila Hospital, Bone Bolango Regency.&#x0D; The results obtained during this research areThe results of a comparison of leukocyte examination using a hematology analyzer and hemocytometer were obtained0.039 &lt;0.05, where the null hypothesis (H0) means rejected and the alternative hypothesis (Ha) means accepted, so it can be concluded that the data obtained is different.
Pengaruh Media Pembelajaran Quipper School terhadap Minat dan Hasil Belajar Siswa pada Mata Pelajaran PAI di SMA Negeri 1 Kinali

The purpose of this study was to determine the effect of Quipper School learning media on students' interest and learning outcomes in PAI subjects. The method used in this study was a Quasi Experiment with The Static Group Comparison design: Randomized Control Group Only Design by applying Quipper School learning media to an experimental class of 35 people and a control class of 36 people. Research data on student interest and learning outcomes were collected by means of questionnaires and tests, then analyzed using the Manova test with the help of SPSS version 22. The findings of this study are hypothesis testing, 1) There is an influence of Quipper School learning media on student learning interest in subjects PAI at SMAN 1 Kinali. Obtained from a significance value smaller than 0.05, namely 0.000 &lt;0.05, which means a significant effect. The results showed that the average score of students' interest in learning in the control class was 67, while the average score of interest in learning in the experimental class was 87, which was in the very high category. 2) There is an influence of Quipper School learning media on student learning outcomes in PAI subjects at SMAN 1 Kinali. Obtained from a significance value smaller than 0.05, namely 0.000 &lt;0.05, which means a significant effect. The results showed that the average score of student learning outcomes in the control class was 67 while the average score of interest in learning in the experimental class was 85. There was a significant difference between the control class and the experimental class. 3) There is an influence of Quipper School learning media on students' interest and learning outcomes in PAI subjects at SMAN 1 Kinali. The results showed that the value of ????ℎi????????????g = 86,904 with ???????????????????????? = 3.29 (????ℎi????????????g &gt; ????????????????????????) Thus ????0 is rejected and accepts 1.0.
DABC: A dynamic ARX-based lightweight block cipher with high diffusion

The ARX-based lightweight block cipher is widely used in resource-constrained IoT devices due to fast and simple operation of software and hardware platforms.However, there are three weaknesses to ARX-based lightweight block ciphers.Firstly, only half of the data can be changed in one round.Secondly, traditional ARX-based lightweight block ciphers are static structures, which provide limited security.Thirdly, it has poor diffusion when the initial plaintext and key are all 0 or all 1.This paper proposes a new dynamic ARX-based lightweight block cipher to overcome these weaknesses, called DABC.DABC can change all data in one round, which overcomes the first weakness.This paper combines the key and the generalized two-dimensional cat map to construct a dynamic permutation layer P1, which improves the uncertainty between different rounds of DABC.The non-linear component of the round function alternately uses NAND gate and AND gate to increase the complexity of the attack, which overcomes the third weakness.Meanwhile, this paper proposes the round-based architecture of DABC and conducted ASIC and FPGA implementation.The hardware results show that DABC has less hardware resource and high throughput.Finally, the safety evaluation results show that DABC has a good avalanche effect and security.
Face Mask Detection Based Entery

The Covid-19 pandemic has introduced several challenges to the society and safety measures are of utmost importance. Hence, to contain and reduce the spread, mask detection-based entry has emerged as a very fascinating topic in the domains of image processing, computer vision, and the Internet of Things (IoT). Convolutional architectures are being used to develop a number of new algorithms that will improve the accuracy of the algorithm. Such convolutional architectures have also made possible the extraction of pixel details. The project aims to build a binary face classifier which can detect the presence of a mask and accordingly people will be granted entry. The classifier is created by using Convolutional Neural Network (CNN), Region based CNN (R-CNN), ThingSpeak.
Enhancement of text categorization results via an ensemble learning technique

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Wasf A. Taha, Suhad A. Yousif; Enhancement of text categorization results via an ensemble learning technique. AIP Conference Proceedings 2 February 2023; 2457 (1): 040011. https://doi.org/10.1063/5.0122942 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Theme structure in an introduction section of articles published in Indonesian national journals

One of the important parts of a research article is an introduction. As the initial part of an article, it should provide all the ideas confirming that the study needs to be conducted. Therefore, the ideas presented should be well organized. One of the ways to confirm a good organization of ideas in the introduction is by paying attention to the theme structure used to build the clauses. This study analyzed the theme types and structure used in the introduction section of articles published in Indonesian national journals. This study was conducted using qualitative content analysis. Using a documentation technique, the data were collected from 15 research articles published in the latest volumes (2020-2021) of four Indonesian national journals. The data were analyzed using a content analysis method, following the established procedure of the clause theme analysis. The results of the study found the dominant use of simple themes in the introduction section of the research articles, indicating a lack of ideas organization. Besides, it was also found that the theme structure of the clauses was realized in nine theme patterns. These patterns are used in presenting different contents of an introduction. It is concluded that theme types and patterns contribute to producing a good quality introduction section of a research article.
Study of a Software Development Team's Adaptations to Remote Work During the COVID-19 Pandemic

The social distancing practices adopted to contain the spread of the COVID-19 pandemic led many companies to migrate to remote work in a compulsory and unplanned way. This sudden transition to working from home has caused profound changes in personal and professional relationships. In this paper, we present the results of a qualitative observational study about the adaptations made in the software process activities of a software development coordinator of a Brazilian university. These adaptations aimed to support the transition to remote work during the pandemic, without letting the organization lose its essence in adopting agile practices. These adaptations were analyzed based on the technical aspects (hard skills) and behavioral aspects (soft skills) of the employees of the researched organization.
Using a Teamwork Quality Instrument to Improve Agile Teams’ Effectiveness: Practical Use Cases

Agile Software Development (ASD) has become the most chosen development method. The core fundamentals of ASD are based on Teamwork factors and how valuable it considers individuals and their interactions over processes and tools. Researchers have shown the positive impact of teamwork quality in ASD and the importance of assessing it to increase the chances of succeeding projects in this context. Based on this, some researchers have proposed instruments that can assess ASD teamwork quality. One of these instruments is a bayesian network-based model (TWQ-BN), with its practical utility assessed in a case study presented in previous work. However, there is a lack of practical use cases documented using TWQ-BN to identify process improvement opportunities. This paper addresses this gap by presenting two industry-based use cases to help potential users understand how to use TWQ-BN to define action items to improve the team’s effectiveness. This paper provides better guidance toward adopting TWQ-BN and shows how it can be used as a tool on iteration retrospectives to diagnose the teamwork quality.
More than Syntaxes: Investigating Semantics to Zero-shot Cross-lingual Relation Extraction and Event Argument Role Labelling

Syntactic dependency structures are commonly utilized as language-agnostic features to solve the word order difference issues in zero-shot cross-lingual relation and event extraction tasks. However, while sentences in multiple forms can be employed to express the same meaning, the syntactic structure may vary considerably in specific scenarios. To fix this problem, we find semantics are rarely considered, which could provide a more consistent semantic analysis of sentences and be served as another bridge between different languages. Therefore, in this paper, we introduce S yntax and S emantic D riven N etwork (SSDN) to equip syntax and semantic knowledge across languages simultaneously. Specifically, predicate-argument structures from semantic role labelling are explicitly incorporated into word representations. Then, a semantic-aware relational graph convolutional network (Sem-RGCN) and a transformer-based encoder are utilized to model both semantic dependency and syntactic dependency structures, respectively. Finally, a fusion module is introduced to integrate output representations adaptively. We conduct experiments on the widely-used ACE2005 English, Chinese, and Arabic datasets. The evaluation results demonstrate that the proposed method achieves the state-of-the-art performance. Further study also indicates SSDN could produce robust representations that facilitate the transfer operations across languages.
Can We Bridge The Divide? Right‐Wing Memes as Political Education

Abstract Many on the contemporary Left assume that the Right has irrevocably taken control of cyberspace. Many believe that the terrain of online memetic discourse, from 4chan to Russian interference in the 2016 election via social media, is now the domain of trolls, fascists, and neo‐Nazis. In this article, Gabriel Keehn argues against that assumption, tracing the ways in which the Right won the meme war and arguing for the educative and liberatory potential of a left counteroffensive in this still contested space.
Symposium Introduction: Building Bridges

Educational TheoryVolume 72, Issue 6 p. 727-730 ORIGINAL ARTICLE Symposium Introduction: Building Bridges Heather Greenhalgh-Spencer, Corresponding Author Heather Greenhalgh-Spencer heather.greenhalgh-spencer@ttu.edu Department of Curriculum and Instruction, Texas Tech UniversitySearch for more papers by this authorAmy B. Shuffelton, Amy B. Shuffelton ashuffe@luc.edu Department of Philosophy, Loyola University ChicagoSearch for more papers by this author Heather Greenhalgh-Spencer, Corresponding Author Heather Greenhalgh-Spencer heather.greenhalgh-spencer@ttu.edu Department of Curriculum and Instruction, Texas Tech UniversitySearch for more papers by this authorAmy B. Shuffelton, Amy B. Shuffelton ashuffe@luc.edu Department of Philosophy, Loyola University ChicagoSearch for more papers by this author First published: 02 February 2023 https://doi.org/10.1111/edth.12556Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume72, Issue6December 2022Pages 727-730 RelatedInformation
Abstract WP123: Machine Learning To Predict High Risk Adverse Events In Treatment Trials For Intracerebral Hemorrhage

Background: ICH clinical trials have shown that aggressive blood pressure (BP) lowering can lead to adverse events (AEs) such as renal failure (RF) or new stroke. These studies focused on whether the study intervention causes AEs on average. By combining trials, we estimate each individual’s risk of developing AEs with BP lowering. Methods: We augmented data from ATACH2 by adding patients from the observational ERICH study and performed propensity score matching with a radius (radius=0.4, ratio=1). We estimated the individualized counterfactual outcome of renal AEs using augmented ATACH2. ERICH had 127 patients (out of 1,706) with new onset of RF during hospital stay. ATACH2 had 68 patients (out of 1000) with renal AEs (recorded by MedDRA) within 7d. We built the counterfactual prediction model using a neural network model (FlexTENet). We extracted feature importance score using Shap Deep Explainer. Results: We borrowed 1,706 patients to augment ATACH2. The augmented cohort had 2706 patients in total. FlexTENet captured the heterogeneity fairly. For factual outcome prediction accuracy, the FlexTENet achieved the best area under precision-recall curve (0.203) and the area under receiver operating curve (0.703). High diastolic BP at admission and low platelets increased the counterfactual effect of BP reduction on occurrence of renal AEs. In addition, high systolic BP and high glucose decreased the counterfactual effect on the occurrence of renal AEs. Conclusions: Identifying treatment-related AEs at an individual level has rarely been studied. Our counterfactual machine learning shows potential to predict individual patient’s risk of treatment-related adverse events.