Reviewerly/exHarmony huggingface dataset:
Evaluation of an Urban Drainage Channel System on Tuamang Road, Medan

The population that continues to grow every time affects the total wastewater that is accommodated and channeled by drainage. This study aims to evaluate the existing condition of drainage dimensions on Tuamang Road, Medan. To determine whether the drainage can still channel the design wastewater discharge for the next 10, 20, and 30-year return periods or if it requires re-planning. The descriptive-evaluative method is used to evaluate drainage with stages starting from collecting data on population, annual rainfall, and measuring the dimensions of drainage channels; hydrological analysis, population projections for 10, 20, and 30-year; and evaluation of drainage channels. The research location is on Tuamang Road with a length of 1,200 m from Domestic House No. 130 to No. 101. The drainage system on Tuamang Road can still channel rainwater runoff and household waste for up to 10-year, based on analysis. 20 and 30-year, the dimensions must be enlarged to 1.0 m (bottom width) and 0.7 m (wet height of the channel). Cleaning sediment at the channel bottom must continue regularly to ensure the drainage system is working properly
Improved Attention Mechanism-Based Object Detection Method

Aiming at the problems of low detection accuracy for objects with non-significant features in the FCOS network, a new object detection method based on attention mechanism is proposed to improve the performance of FCOS network, which can effectively guide the network to focus on the detailed features. According to the verification experimental results on KITTI dataset, the AP value of the improved attention mechanism-based method for car and person detection is improved by 1.1% and 4.9% compared with the standard FCOS network, respectively, and the average category accuracy value is improved by 3%. Thus, the experimental results show the effectiveness of the proposed method.
Lattice Linear Discriminant Analysis for Shape Constrained Classification

Recently shape constrained classification has gained popularity in the machine learning literature in order to exploit extra model information besides raw data features. In this paper, we present a new Lattice Linear Discriminant Analysis (Lattice-LDA) classifier, which allows to take shape constraints of data inputs, such as monotonicity and convexity/concavity. Lattice-LDA constructs a nonparametric nonlinear discriminant hyperplane for classification, using an additive format of 1-D lattice functions (piecewise linear functions). Moreover, the new classifier features in taking complex shape constraints including combinations of shapes or S-shape. We optimize the model parameters using the Adaptive Moment Estimation (Adam) algorithm embedding stepwise projections which guarantee feasibility of the shape constraints. Through simulation and real-world examples, we demonstrate that the new classifier could accurately recover the nonlinear marginal effect functions and improve classification accuracy when additional shape information is present.
Effectiveness of Localized Reading Activity Sheets in Enhancing the Reading Skills of Grade 1 Learners A Quasi-Experimental Research Design

Covid19 pandemic has brought fears, challenges, and great changes to all sectors, not only in the Philippines but the entire world. The most affected sector would be education since the learning must continue despite the pandemic. It is a great challenge to pursue education and deliver a quality one, especially since the strategies to be used are novel, and teachers should be trained well before their implementation. This study aims to determine the effectiveness of localized Reading Materials among Grade 1 learners through a quasi-experimental research design. The two groups of participants serve as the comparison and experimental groups. A match-pairing technique was used to identify the 30 participants for each group. Weighted average mean, standard deviation, t-test, and independent t-test were utilized to analyze the data. Likewise, Cohen's D was used to determine the effect size or the size of the difference between the two test results. The findings revealed that most of the respondents in the experimental group obtained a higher mean score, while the comparison group was on average. The composite mean score and standard deviation obtained in the experimental and comparison group with a mean score and standard in the descriptive interpretation of experimental and comparison were average. The t-value yielded in the comparative analysis of formative scores of the experimental and comparison groups. The formative mean score of the Effect Size of Large implies the large statistical formative mean score of experimental and comparison groups.
Effectiveness of Home Economics Learning Commons (HELCs) in Enhancing the Academic Performance of Grade 5 Learners: A Quasi-Experimental Research

This quasi-experimental research design determined the effectiveness of the Home Economics Learning Commons (HELCs) in improving the academic performance of Grade 5 learners. It utilized the formative, summative, and post-tests as part of the research instruments. Match-pairing technique was used to determine the participants of the study. The said participants were the Grade 5 pupils from five different Cabuyao Elementary Schools. The comparison group consisted of students enrolled in the Online Distance Learning modality, while the experimental group consisted of students enrolled in the Modular Distance Learning modality. Based on the data gathered, it revealed that the mean scores in the formative test were high in both the comparison and experimental groups, while the mean scores in the summative test varied from 54-73% and were interpreted as average to near to mastery; and mean scores in the post-test ranged from 14-18.50%, considered as average to high. There was a substantial difference in the Pre-test and Post-test Mean Scores of the five participating schools' Comparison and Experimental Groups, according to the results of the Paired t-Test. Hence, the study concluded that there is a significant difference between the formative tests' mean scores of the two groups. Likewise, there is a significant difference between the post-test mean scores of the two groups. Lastly, there is a significant difference between the pre-test and post-test mean scores of each group. As a result, it was established that HELCs have a considerable positive effect on students' academic achievement.
Mining Local Tight Spatial Sub-Prevalent Co-Location Patterns

Spatial sub-frequent co-location patterns reveal the rich spatial relation-ship of spatial features and instances, which are widely used in real applications such as environmental protection, urban computing, public transportation, and so on. Existing sub-frequent pattern mining methods cannot distinguish patterns whose row instance spatial distributions are significantly different. Additionally, patterns whose row instances are tightly located in a local area can further reveal the particularity of the local area such as special environments and functions. Therefore, this paper proposes mining Local Tight Spatial Sub-frequent Co-location Patterns (LTSCPs). First, a relevancy index is presented to measure the local tightness between sub-frequent pattern row instances by analyzing mutual participation instances between row instances. The concept of LTSCPs is then proposed followed by an algorithm for mining these LTSCPs. Finally, a large number of experiments are carried out on synthetic and real datasets. The results show that the algorithm for mining LTSCPs is efficient and LTSCPs are practical.
A New Student Behavior Analysis Method Based on K-Means Algorithm and Consumption Data of Campus Smart Card

In order to provide decision support for students’ education management, a new student behavior analysis method based on the K-means algorithm and consumption data of campus smart card is proposed in this paper. An optimized Apriori algorithm is used to analyze the relationship between consumption behavior and academic performance. This method effectively provides management decision support for student managers, and improves management efficiency. This should improve the process of intelligent management.
Forensic Handwriting Analysis by Digital Photographic Copies of Documents (Part 2)

The article presents the results of the second stage of the development of methodological guidelines for the forensic handwriting research of handwriting objects in digital copies of documents. This publication is a continuation of the article from 2020 [1]. The author discusses some technological aspects of digital photographing and scanning, as well as technical characteristics of a digital photograph (scan), offers general conceptual clarifications related to the study of handwriting implementations in digital copies of documents; defines the qualitative characteristics of handwriting objects that determine the admissibility of handwriting research and presenting efficient conclusions; lists the factors reducing the quality indicators of digital images, and signs pointing to them; proposes the rules and conditions for digital copying of documents for the most optimal quality of handwriting objects in order to conduct identification and diagnostic studies; considers the algorithms of actions for a handwriting expert when working with images of digital handwriting objects submitted for research in the form of a computer file.
Analisis Keefektifan dan Kemudahan Implementasi IT Governance di Instansi X

Information Technology (IT) is an important aspect of supporting the success of an organization in the digital business era, so IT governance is required to ensure the alignment of business and IT in achieving company goals. Institution X is one of the government institutions that already implemented IT governance based on the COBIT 5 framework in their organizational governance structure. Nevertheless, various risks are still found in IT operational activities such as project delay and resource allocation problems. This problem raises the question of IT governance implementation effectiveness and easiness. This research was conducted quantitatively to analyze this question. The research analysis descriptively considers the structure, process, and relational mechanism perspectives. From this research can be concluded that the implementation of IT governance in Institution X has been effective and easy. Information Technology (IT) merupakan aspek penting dalam mendukung keberhasilan organisasi di era bisnis digital, sehingga IT governance dituntut untuk memastikan adanya keselarasan bisnis dan IT dalam mencapai tujuan perusahaan. Instansi X merupakan salah satu lembaga pemerintah yang sudah mengimplementasikan IT governance yang mengacu pada framework COBIT 5 dalam tatanan tata kelola organisasi. Namun pada praktiknya, masih ditemukan berbagai risiko dalam kegiatan operasional IT seperti keterlambatan proyek dan kurang optimalnya alokasi sumber daya. Hal ini menimbulkan pertanyaan terkait keefektifan dan kemudahan implementasi IT governance. Penelitian ini dilakukan secara kuantitatif untuk menganalisis keefektifan dan kemudahan implementasi IT governance. Analisis penelitian ini secara deskriptif mempertimbangkan perspektif struktur, proses, dan mekanisme relasional. Dari penelitian yang sudah dilakukan, dapat disimpulkan bahwa implementasi IT governance di Instansi X sudah efektif dan mudah.
Sistem Akses Masuk Perpustakaan Menggunakan Sistem Pengenalan Wajah dan Pengukuran Suhu Tubuh Berbasis Single Board Computer (Studi Kasus Perpustakaan Unand)

The library is one of the facilities used as a place to obtain information, improve literacy, and develop knowledge and insight. The level of security that is still lacking causes the high threat of crime caused and the spread of the virus which is still difficult to control, so this research was carried out by forming a system that could facilitate entry with facial recognition and body temperature measurement to improve the security system and be able to suppress the spread of the virus.The system will open a portal for entry access when the face image is recognized with a normal body temperature without any physical contact, while when the face is not recognized the system will provide a notification that the detected face image is not recognized.
Manajemen Strategi dalam Meningkatkan Keberhasilan Proyek Sistem Informasi Menggunakan Analisis Critical Success Factor

Strategic management is needed to increase the success of information systems projects to increase work efficiency. The purpose of this research is to analyze the factors that influence the success of an information system project using 18 critical success factors which are grouped into 4 factors. Collecting data using a questionnaire with 100 IT employees in Jakarta. Furthermore, for data processing using the multiple linear regression analysis methods. This research shows that project management, team capability, user involvement, and project methodology have a positive and significant impact on project success. Manajemen strategi diperlukan untuk meningkatkan keberhasilan proyek sistem informasi agar dapat meningkatkan efisiensi pekerjaan. Tujuan dari penelitian adalah untuk menganalisis faktor-faktor yang memengaruhi keberhasilan proyek sistem informasi dengan menggunakan 18 critical success factors yang dikelompokkan menjadi 4 faktor. Pengumpulan data menggunakan kuesioner dengan 100 karyawan IT di Jakarta. Selanjutnya untuk pengolahan data menggunakan metode analisis regresi linear berganda. Pada penelitian ini menunjukkan bahwa manajemen proyek, kapabilitas tim, keterlibatan pengguna, dan metodologi proyek berpengaruh positif dan signifikan terhadap keberhasilan proyek.
Scrapbook Learning Media to Improve Learning Outcomes of My Nation's Cultural Diversity Materials for Grade IV Elementary School Students

The social studies learning process is usually carried out in general and the teacher is the main source, which is caused by the lack of use of learning media, resulting in less-than-optimal student learning outcomes. This study aims to develop Scrapbook learning media to improve student learning outcomes on the material of my nation's cultural diversity in fourth grade elementary school students. The method used in this research is R&amp;D (Research and Development) with a 4-D development model (Define, Design, Development and Disseminate). The subjects in this study were 17 students, and 3 people as material experts, linguists and media experts. The data collection method used is an expert validation sheet and a student discussion sheet in the form of multiple choice. The results of the feasibility of the validation of the experts, namely the assessment of the material expert obtained a score of 90.91%, the assessment of the linguist obtained a score of 86.67%, and the assessment of the media expert obtained a score of 100%. Based on the overall results of the validation obtained a score of 92.52% and is included in the category very suitable for use in learning. The results of the N-Gain value obtained are 0.58 and are included in the medium category. Based on the N-Gain value obtained, it can be concluded that the Scrapbook learning media is quite effective and feasible to use to improve student learning outcomes.
Image Captioning Using Deep Learning and NLP Techniques

Abstract: The use of text to explain an image is known as picture captioning. It's usually used in applications that require information from a certain image to be automatically created in a textual format. This research tackles limitations by developing a vocabulary for interpreting visuals so that complicated, unified stories may be told. It uses an LSTM model with an NLP method to discover linguistic regions in pictures and builds a model that decomposes both photographs and captions into their constituent elements. It also demonstrates how to apply the LSTM Method with enhanced features for better results. The Flicker 8K dataset is used in this project
Semantic Web Application: Tourist Assisting Web Application

Abstract: In addition to traditional web technologies, semantic web technology allows the implementation of the "web of things" and "web of linked data," enabling both machines and humans to access the web. The ultimate objective is to allow machines to do increasingly complex tasks and to design systems that can support network interactions. This goal may be realized by merging several linked data technologies, such as RDF, SPARQL, OWL, and SKOS (W3C Org, 2014). In this project, the authors integrated semantic web technologies into the software to illustrate their use. This study describes in detail the design and functionality of the apps, as well as how the semantic web technology principles were implemented. The authors followed the principles and best practices outlined in W3C documents and directed by the first author. The authors exerted a great deal of work to evaluate the acquired data and have begun the project by locating suitable datasets from various data sources (heterogeneous data sources). Following this, more attempts were made to cleanse and verify the data. This generated dataset was subsequently pushed to the selected triple store on application start-up. The ontology design and project architecture are described in full in this study. The project's stack and implementation has been discussed in this report.
A graph-transformer for whole slide image classification

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading. Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade. We selected $4,818$ WSIs from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the National Lung Screening Trial (NLST), and The Cancer Genome Atlas (TCGA), and used GTP to distinguish adenocarcinoma (LUAD) and squamous cell carcinoma (LSCC) from adjacent non-cancerous tissue (normal). First, using NLST data, we developed a contrastive learning framework to generate a feature extractor. This allowed us to compute feature vectors of individual WSI patches, which were used to represent the nodes of the graph followed by construction of the GTP framework. Our model trained on the CPTAC data achieved consistently high performance on three-label classification (normal versus LUAD versus LSCC: mean accuracy$= 91.2$ $\pm$ $2.5\%$) based on five-fold cross-validation, and mean accuracy $= 82.3$ $\pm$ $1.0\%$ on external test data (TCGA). We also introduced a graph-based saliency mapping technique, called GraphCAM, that can identify regions that are highly associated with the class label. Our findings demonstrate GTP as an interpretable and effective deep learning framework for WSI-level classification.
Stacked-CNN-BiLSTM-COVID: An Effective Stacked Ensemble Deep Learning Framework for Sentiment Analysis of Arabic COVID-19 Tweets

Abstract Social networks are popular for advertising, idea sharing, and opinion formation. Due to COVID-19, coronavirus information disseminated on social media affects people's lives directly. Individuals sometimes managed it well, but it often hampered daily activities. As a result, analyzing people's feelings is important. Sentiment analysis identifies opinions or sentiments from text. In this paper, we present an effective model that leverages the benefits of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to categorize Arabic tweets using a stacked ensemble learning model. First, the tweets are represented as vectors using a word embedding model, then the text feature is extracted by CNN, and finally the context information of the text is acquired by BiLSTM. Aravec, FastText, and ArWordVec are employed separately to assess the impact of the word embedding on the our model. We also compare the proposed method to various deep learning models: CNN, LSTM, and BiLSTM. Experiments are performed on three different Arabic datasets related to COVID-19 and vaccines. Empirical findings show that the proposed model outperformed the other models' results by achieving F-measures of 76.76\%, 87.\%, and 80.5\% on the SenWave, AraCOVID19-SSD, and ArCovidVac datasets, respectively.
Investigating the Security Threats on Using M-Payment Applications in Saudi Arabia: Exploratory Study

Online banking, debit cards, credit cards and mobile payments are the most common payment types in Saudi Arabia. This study explored security threats that affect m-payment applications in Saudi Arabia by interviewing 16 IT professionals to explore their insights and opinions about those security threats. Cybersecurity threats present the biggest challenge for most mobile systems, as mobile payments can be affected by cyber-attacks and require sophisticated approaches to achieve the desired security. In our study we report on the impact of security threats on the utilization of mobile payment applications and provide evidence related to those threats and their impact on the use of mobile payment applications. Evidence was provided regarding the security threats and their impact on using mobile payment applications. Information was also provided related to security threats such as Distributed Denial-of-Service, phishing attacks and malware. Although the participants in this study demonstrated a positive attitude regarding the safety and security of mobile payments, they also highlighted the security threats that impact m-payments. The results showed that the three main threats in Saudi Arabia were Distributed Denial-of-Service (DDoS), phishing attacks and Malware (Malicious software). This study makes two contributions. The first is to theory, by filling the gap in the literature because it is the first study to explore the threats to using m-payment in Saudi Arabia. Secondly, this study contributes to practice by providing a clear picture for service providers and users about threats they may face when using m-payment.
Smart Trolley for Surya Janti Supermarkets Slahung District Based on ATMega 328p

Shopping in terms of shopping is an activity of the community that purchases a need either in the market or in the market. According to the Director-General of Domestic Trade, Suhanto, the growth of consumption of retail goods or referred to as FMCG (Fast Moving Customer Goods) from April 2018 to April 2019 in Indonesia grew positively by 1.8% compared to the previous year with details for the minimarket format growing by 12,1%. With the increasing number of buyers in supermarkets, there will be queues at the cashier when scanning goods during payment. In addition, buyers often have difficulty in knowing product descriptions and total spending, so they cannot estimate total spending with the money owned by buyers, as happened at Surya Janti Supermarket, Slahung District. Ponorogo Regency. From this research, the Smart Trolley for Surya Janti Supermarkets, Slahung District Based on ATMega 328p solves problems in supermarkets. This smart trolley is equipped with a barcode scanner as data input, an LCD that will display a description of the goods and the total shopping, and an ATMega328p Microcontroller as the main control of the trolley. This trolley is also equipped with a door that will open by pressing the keypad to minimize fraud during the scanning process and is integrated with a telegram as storage for transaction data that will be checked again by the cashier.