Reviewerly/exHarmony huggingface dataset:
Security Awareness Strategy for Phishing Email Scams: A Case Study One of a Company in Singapore

Social Engineering Procedures and phishing are some of the standard procedures and problems today, mainly through sophisticated media such as email, the official means of communication companies use. Phishing emails are usually associated with Social Designing. They can be sent via joins and connections in this email, but they are not secure. Proliferation can be hacked into private/confidential data or total control over the computer/Email without the client's knowledge. The method used in this research is a cycle that will run continuously in a life cycle, starting from problem identification, then generating ideas and evaluating the Implementation of solutions. At each stage, a thorough checking process is needed to obtain results. Follow what you want. Achieved. The results of this study provide recommendations and some suggestions that companies can make; this aims to be one of the doors that provides restrictions for access from parties who are not entitled to access the application. Some thought has shown that this attack is growing and affecting the population. The evaluation stages in this study consist of 5 phases. Each phase is a step used to prevent both the system and the behavior in the company. Awareness is critical at the start considering this is the basis for the organization to determine who will take care of the personnel's knowledge related to information security. It thinks about using survey writing strategies and recommendations that can be made in anticipation of an attack, such as setting up representation or attention as early and often as possible.
Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method

Performance allowance is a form of appreciation given by an agency to its human resources. The Office of the Ministry of Religion of Batu City provides performance allowances to civil servants who work in the agency. Several things that affect the provision of performance allowances, such as grade, deduction, taxable income, income tax, and total tax, are used in this study to produce the total gross performance allowances and total performance allowances received. Based on the data obtained, there are some missing data from the parameters of taxable income, income tax, and total tax. This study aims to predict performance allowance when there is missing data. The method used is Neural Network Backpropagation. This study uses 480 data with split data ratios of 50:50, 60:40, 70:30, and 80:20, with epochs 40,000 and a learning rate 0,9. Four types of models used in this study are distinguished based on the number of hidden layers and epochs used. Model A uses two hidden layers to produce the highest accuracy with a 50:50 data split ratio of 65,16%. Model B uses four hidden layers to produce the highest accuracy with a 50:50 data split ratio of 69,34%. Model C uses six hidden layers to produce the highest accuracy with a 50:50 data split ratio of 68,18%. Model D uses eight hidden layers to produce the highest accuracy with a 50:50 data split ratio of 70,90%.
Research Trends in Politics of Education in U.S. (II): Focusing on thematic analysis of the period of academic leaps and settlements

교육정치학은 제2차 세계대전 이후 등장하기 시작한 이래로 1969년 미국 교육정치학회 출범, 1987년 PEA 연보 및 EP 저널 출간, 1997년 AERA의 분과 L로의 자리매김 등을 거치며 교육학의 분과학문으로 입지를 확고히 하고 있다. 미국 교육정치학의 발전 경로는 꾸준히 한국 교육정치학 연구에 다방면의 유의미한 시사점을 제공한바, 출범 30주년을 앞둔 우리 교육정치학의 향후 발전 방향 모색을 위하여 미국 교육정치학의 연구동향을 살펴볼 필요가 있다. 이에 본 연구는 신현석(1994) 연구의 후속으로써 미국 교육정치학회 연보(PEA Yearbook)가 발간된 시기를 기점으로 도약기(1987~1996), 정착기(1997~현재)로 구분하여 시대적 배경과 연구주제 분석을 통해 연구동향을 분석하였다. 분석결과, 미국 교육정치학의 도약기는 교육개혁을 중심으로 교육정책, 교육행정, 경제적 관점에서의 교육과 불평등, 교육공동체 등의 주제를 탐구하는 방향으로 연구가 진행되었다. 정착기는 도약기와 유사하게 교육개혁이라는 큰 틀에서 논의가 전개되었으나 분야· 집단에 대한 새로운 관심 확대와 함께 사회정의·두려움·분산 등과 같은 비가시적 주제로의 확장이 확인되었다. 연구결과를 바탕으로 고등교육, 영유아, 학생 등과 같은 다양한 연구영역으로의 확대 및 겉으로 드러나지 않는 비가시적 영역에의 관심을 기울일 것을 제언하였다.This study analyzes the research trends in politics of education in U.S, distinguishing between two periods characterized by academic leaps and settlements. Since its slow emergence after World War II, the politics of education in U.S. has established itself as a branch of educational studies with the founding of the Politics of Education Association in 1969, the publication of the PEA Annals and EP Journal in 1987, and the establishment of AERA’s Division L in 1997. The development path of the politics of education in U.S. has continued to have important implications for the study of politics of education in Korea in a variety of ways. In order to explore the future development of Korean Society of the Politics of Education (KSPE) ahead of its 30th anniversary, it is necessary to look at the research trends of politics of education in U.S. As a follow-up to Shin (1994), this study analyzes research trends by dividing the period of academic leaps (1987-1996) and settlements (1997-present) based on when the PEA Annals were published. As a result of the analysis, the leaps period was centered on education reform and explored topics such as education policy, education administration, education from an economic perspective, inequality, and educational communities. The settlements period was similar to the leapfrog period, with discussions of education reform, but with a renewed interest in sectors and groups, and an expansion into invisible topics such as social justice, fear, and distraction. Based on our findings, the study suggests expanding into different areas of research, such as higher education, early childhood, and students, and paying attention to invisible areas.
MAFA-Net: Multimodal Attribute Feature Attention Network for visual question answering

Abstract Visual Question Answering (VQA) is a hot topic task to answer natural language questions related to the content of visual images. In most VQA models, visual appearance and attribute features are ignored, resulting in complex questions without correct answers.To solve these problems, we propose a new end-to-end VQA model called Multi-modal Attribute Feature Attention Network (MAFA-Net).Firstly, the self-guided word attention modulus is designed to connect entity words with semantic words. Secondly, two problematic adaptive visual attention modules are presented not only to extract important regional features, but also to focus on key attribute features (e.g., color, spatial relationships, etc.). Additionally, a combining strategy is proposed to better explore spatial relationships between objects and their appearance properties. Finally, the experimental results show that MAFA-Net achieves performance competitive with state-of-the-art models on two large-scale VQA datasets.
Artificial Intelligence-Based Chatbot to Support Public Health Services in Indonesia

The aim of this study is to build an artificial intelligence chatbot application to support public health services. The chatbot acts as an information service that can replace the role of humans. The analysis of functional needs was obtained from information submitted by one of the heads of public health centers in Indonesia. This study uses the Scrum method with pregame stages to produce a plan consisting of functional and non-functional requirements analysis and conceptual design of the chatbot, which will be developed using Unified Modeling Language (UML) diagrams. The process of finding answers uses the matching graph master technique, which is a backtrack matching that utilizes a depth-first search strategy. There are 6 topics of chatbot services, including service schedules, health information, registration, diseases, drugs, and early care services for chatbot users. Tests conducted on these 6 topics showed an average correct answer ratio of 93.1% out of a total of 251 questions. The result of the usability measurement on the chatbot application that has been built obtained a system usability scale value of 80.1, indicating that the developed chatbots are acceptable for use.
Lowering the cost of fault-tolerant Quantum Comparator Circuits

Abstract Quantum comparators hold substantial significance in the scientific community as fundamental components in a wide array of algorithms. In this research, we present an innovative approach where we explore the realm of comparator circuits, specifically focussing on three distinct circuit designs present in the literature. These circuits are notable for their use of T-gates, which have gained significant attention in circuit design due to their ability to enable the utilisation of error-correcting codes to ensure fault-tolerance in quantum circuits. However, it is important to note that T-gates come at a considerable computational cost. Our methodology revolves around a comprehensive study of these comparator circuits from a bottom-up perspective. We delve deep into the intricate details of these circuits, dissecting their underlying components and functions. This meticulous examination forms the foundation of our research, enabling us to identify areas where optimisations can be made to improve their performance. One of the key contributions of our work is the optimisation of the quantum gates used within these circuits. We have expressed the proposed circuits in Clifford+T gates to render them fault-tolerant while concurrently minimising the utilisation of T-gates, thereby mitigating the associated computational costs. By strategically reducing the number of T-gates used and improving their efficiency, we not only mitigate computational overhead, but also ensure the robustness of these circuits in the face of errors and environmental disturbances.
Implementasi Algoritma Fuzzy C-Means dalam Mengelompokkan Produktivitas Nauplius Vanammei di BBPBAP Jepara

Balai Besar Perikanan Budidaya Air Payau often experiences changes in the number of vanammei shrimp production that is uncertain every day. This resulted in the scarcity of vanammei shrimp larvae seeds, unmet market demand and high selling prices. So that the BBPBAP experienced problems in monitoring the quality of the production of vanammei shrimp larvae. To overcome these problems, data mining is used as an alternative solution. The research method used is the Fuzzy C-Means algorithm. With the research stages starting from data collection, pre-processing data to clean data or attributes that are not suitable, clustering using the Fuzzy C-Means algorithm, testing using the MATLAB tool, and evaluating validation using the Davies Bouldin Index (DBI). Based on the results of the clustering that has been carried out from a total of 894 data, which are included in the good cluster, 569 data with the unfavorable category is 325. The results of the evaluation and validation obtained a value of 0.32 with the resulting cluster quality is optimum.
StockWatch: Insilico CEO Breaks Down Exelixis Deal

GEN EdgeVol. 5, No. 1 TopicsStockWatch: Insilico CEO Breaks Down Exelixis DealAlex Zhavoronkov discusses USP1's appeal to AI drug developer, pipeline updatesAlex PhilippidisAlex PhilippidisAlex Philippidis is Senior Business Editor of GEN.Search for more papers by this authorPublished Online:25 Sep 2023https://doi.org/10.1089/genedge.5.1.125AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 5Issue 1Jan 2023 Information© 2023 by GEN PublishingTo cite this article:Alex Philippidis.StockWatch: Insilico CEO Breaks Down Exelixis Deal.GEN Edge.Jan 2023.644-651.http://doi.org/10.1089/genedge.5.1.125Published in Volume: 5 Issue 1: September 25, 2023PDF download
A novel method for detecting URLs phishing using hybrid machine learning algorithm

Abstract— The phishing attack is the type of cyberattack that targets people’s trust by masking the malicious intent of the attack as communications from reputable sources. The goal is to steal sensitive data from the victim(s) (banking information, social identification, credentials, etc.) for various purposes (selling for monetary gain, performing identity thief, using as a lever for escalation attack). In 2022, the number of reported phishing attacks will reach a whopping 255 million cases, an increment of 61% compared to 2021. Existing methods of phishing URL detection have limitations. The article proposes a method to increase the accuracy of detecting malicious URL by using machine learning methods Linear Support Vector Classification and multinomial Naive Bayes with voting mechanisms.
The Power of Words: An Overview of Large Language Models (LLMs) and Their Significance in AI

the ever-evolving landscape of artificial intelligence (AI), one class of models has emerged as a game-changer: Large Language Models (LLMs). These models, characterized by their massive size and complexity, have transformed the field of natural language processing (NLP) and have far-reaching implications for a wide range of applications. In this article, we'll take a deep dive into LLMs, exploring what they are, how they work, and their significance in the world of AI.
Design of a 3600 Virtual Reality Website for the Department of Electrical Engineering Sriwijaya State Polytechnic

Information technology plays a crucial role in meeting the aspirations of people seeking knowledge in higher education institutions. One effective strategy to showcase the unique features of each university is by employing a Virtual Reality Website 3600. Through this Virtual Reality Website, prospective students, visitors, and the general public can experience a more immersive understanding of the facilities and lecture rooms in the Department of Electrical Engineering at Sriwijaya State Polytechnic (POLSRI). Users can feel as if they are physically present and interact with the college environment virtually using the 3600 camera technology. In this research, to ensure the security of user data and prevent potential security threats, a One-Time Password (OTP) security mechanism is implemented on the website. The OTP system provides temporary access with unique codes generated each time a user accesses the website. This measure is taken to safeguard user data from unauthorized access and potential information leaks. The outcomes of this study will contribute to introducing users to the Virtual Reality Website 3600 technology and aid the Department of Electrical Engineering at POLSRI in virtually showcasing their facilities and lecture rooms to prospective students in an engaging manner.
When JavaBeans Meet Blockchain: Brewing a Robust Healthcare Data Management System

&lt;p&gt;In the contemporary digital age, the healthcare sector is inundated with a plethora of data, emanating from various sources such as Electronic Health Records (EHRs), wearable devices, and telemedicine platforms. The management, security, and efficient utilization of this data present a formidable challenge, especially considering the stringent regulatory and ethical standards governing healthcare data. This research explores an innovative concoction, blending the robustness of JavaBeans with the immutable and decentralized nature of blockchain technology, aiming to optimize healthcare data management. The study delves into the development of a secure, scalable, and interoperable data management system that ensures the confidentiality, integrity, and availability of healthcare data, while also facilitating seamless data sharing among authorized entities. Through the integration of Java and blockchain technology, this research seeks to navigate through the complexities of healthcare data management, providing a platform that not only adheres to regulatory compliance but also enhances the quality of healthcare delivery. The proposed system leverages the object-oriented and platform-independent features of Java, coupled with the secure and transparent attributes of blockchain, to establish a healthcare data management system that is secure, user-friendly, and adaptable to the evolving healthcare landscape. This research encompasses the systems design, development, implementation, and evaluation, providing insights into its efficacy, challenges encountered, and potential for future adaptations and scalability in the ever-dynamic healthcare domain. &lt;/p&gt;
THE EFFECTIVENESS OF FREQUENCY IN GIVING ASSIGNMENTS ON ENGLISH LEARNING OUTCOMES AT GRADE SIXTH STUDENTS OF GLOBAL PERSADA MANDIRI ELEMENTARY SCHOOL

Purpose of The Research is to find out the effectiveness of frequency in giving assignments on English learning outcomes at grade 6th students at Global Persada Mandiri Elementary School Bekasi. The instrument for obtaining research data was the results of test given to students in two different classes with different methods of giving. The method of this research is experiment method, the population was all class VI students at SD Global Persada Mandiri Bekasi. This research involving 60 samples which taken randomly. Based on the result of the research conducted, it is proven that there is an effect of the frequency of assignments on English learning outcomes with the results of the control class research calculations where the mean value is (65,9), median (64,6), and the mode(65,1) for the experimental class obtained a mean value of (73,9), median (71,2) and mode (72,5). Then the results of the hypothesis test a fairing of 3,88 and of 1,644. In testing the H0 it is rejected if is greater than . The results of the t-test show that &gt; where 3,88&gt;1,644. So it can be concluded that there is an effect of the frequency in giving assignments on the English learning outcomes at grade sixth of Global Persada Mandiri Bekasi. ABSTRAKTujuan Penelitian adalah untuk mengetahui efektivitas frekuensi pemberian tugas terhadap hasil belajar bahasa Inggris pada siswa kelas 6 SD Global Persada Mandiri Bekasi. Instrumen untuk memperoleh data penelitian adalah hasil tes yang diberikan kepada siswa di dua kelas yang berbeda dengan metode pemberian yang berbeda. Metode penelitian ini adalah metode eksperimen, populasinya adalah seluruh siswa kelas VI SD Global Persada Mandiri Bekasi. Penelitian ini melibatkan 60 sampel yang diambil secara acak. Berdasarkan hasil penelitian yang dilakukan terbukti terdapat pengaruh frekuensi penugasan terhadap hasil belajar bahasa Inggris dengan hasil perhitungan penelitian kelas kontrol dimana nilai mean (65,9), median (64, 6), dan modus (65,1) pada kelas eksperimen memperoleh nilai mean (73,9), median (71,2) dan modus (72,5). Kemudian hasil uji hipotesis fairing sebesar 3,88 dan sebesar 1,644. Dalam pengujian H0 ditolak jika lebih besar dari . Hasil uji t menunjukkan &gt; dimana 3,88&gt;1,644. Jadi dapat disimpulkan bahwa terdapat pengaruh frekuensi pemberian tugas terhadap hasil belajar bahasa Inggris kelas VI Global Persada Mandiri Bekasi.
Workforce Development

Nursery WorldVol. 2023, No. 10 PracticeWorkforce DevelopmentChloe BarnesChloe BarnesSenior workforce and development manager at Monkey Puzzle Day Nurseries, on the group's staff development opportunitiesSearch for more papers by this authorChloe BarnesPublished Online:11 Oct 2023https://doi.org/10.12968/nuwa.2023.10.22aAboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 October 2023Volume 2023Issue 10ISSN (print): 0029-6422ISSN (online): 2059-9358 Metrics History Published online 11 October 2023 Published in print 2 October 2023 Information© MA Education LimitedPDF download
DocXClassifier: Towards a Robust and Interpretable Deep Neural Network for Document Image Classification

&lt;p&gt;Model interpretability and robustness are becoming increasingly critical today for the safe and practical deployment of deep learning (DL) models in industrial settings. As DL-backed automated document processing systems become increasingly common in business workflows, there is a pressing need today to enhance interpretability and robustness for the task of document image classification, an integral component of such systems. Surprisingly, while much research has been devoted to improving the performance of deep models for this task, little attention has been given to their interpretability and robustness. In this paper, we aim to improve upon both aspects and introduce DocXClassifier, an inherently interpretable deep document classifier that not only achieves significant performance improvements over existing approaches in image-based document classification, but also holds the capability to simultaneously generate feature importance maps while making its predictions. Our approach attains state-of-the-art performance in image-based classification on two popular document datasets, RVL-CDIP and Tobacco3482, with top-1 classification accuracies of 94.17% and 95.57%, respectively. Additionally, it sets a new record for the highest image-based classification accuracy on Tobacco3482 without transfer learning from RVL-CDIP, at 90.14%. In addition, our proposed training strategy demonstrates superior robustness compared to existing approaches, significantly outperforming them on 19 out of 21 different types of novel data distortions, while achieving comparable results on the remaining two. By combining robustness with interpretability, DocXClassifier presents a promising step towards the practical deployment of DL models for document classification tasks.&lt;/p&gt;
Collegiate cheating: understanding the prevalence, causes, and consequences

This article summarizes the arguments and counterarguments regarding the issue of academic dishonesty. The study’s main aim is to understand and initiate discussions about the widespread but disturbing problem of attrition in higher education institutions around the world. A systematization of literary sources and approaches to understanding university attrition has shown that a deep-rooted interplay of cultural, cross-cultural and social values contributes to academic dishonesty. The relevance of this issue and decisions that are considered necessary in this direction is that it will help educational institutions think about the need to develop a policy of academic integrity and control over strict compliance with the norms of this policy. The work uses a dual methodological approach involving quantitative and qualitative research methods. Within the framework of the quantitative method, a survey of students was conducted, which included a sample of 503 respondents. The purpose of the structured study of students is to determine their general perception of academic integrity, as well as to assess the extent of their use of information technology and compare it with their individual level of academic integrity. The qualitative method involved conducting interviews (in person and by telephone) and 25 focus group discussions among educational administrators regarding the prevalence of academic dishonesty in the academic environment. The internal consistency of the method was checked using Cronbach’s alpha coefficient. The article empirically confirms the results, which indicate that the traditional learning environment, the level of affluence in the family, and technological dependence contributed to non-compliance with the principles of academic dishonesty and a decrease in the quality of educational services. The results showed that the presence of a family business can significantly affect a student’s thinking and form a low level of tolerance for academic dishonesty (F=1.54, p=0.02). With the rapid spread of digital technology, Generation Z students depend highly on technology to support their educational endeavours (F=1.35, p=0.04). The results of the study offer a better understanding of the consequences of deviant academic behavior, the justification for increasing the responsibility of the subjects of the educational process for non-compliance with the principles of academic integrity and the importance of the institutional policy of the educational institution in regulating this issue.