Reviewerly/exHarmony huggingface dataset:
A Heterogeneous Machine Learning Ensemble Framework for Malicious Webpage Detection

The growing dependence on digital systems has heightened the risks posed by cybersecurity threats. This paper proposes a new method for detecting malicious webpages among several adversary activities. As shown in previous studies, malicious URL detection performance is significantly affected by the learning dataset features. The overall performance of different machine learning models varies depending on the data features, and using a particular model alone is not always desirable in any given environment. To address these limitations, we propose an ensemble approach using different machine learning models. Our proposed method outperforms the existing single model by 6%, allowing for the detection of an additional 141 malicious URLs. In this study, repetitive tasks are automated, improving the performance of different machine learning models. In addition, the proposed framework builds an advanced feature set based on URL and web content and includes the most optimized detection model structure. The proposed technology can contribute to define an advanced feature set based on URL and web content and includes the most optimized detection model structure and research on automated technology for the detection of malicious websites, such as phishing websites and malicious code distribution.
Teacher Self-Education in a Pedagogical Context: Posthistory

The importance of the topic of the article lies in the fact that in our time there is a need for innovation of the educational process, the use of innovations in teacher self-training is therefore important teacher self-education in a pedagogical context. The purpose of the article is the need to explore and substantiate the importance of teacher self-education in a pedagogical post-historical context. The article provides a theoretical foundation of the concepts of self-education and self-development of the teacher in the pedagogical context the conditions of the effectiveness of self-education of teachers in the current system of education are highlighted; the structure of readiness for self-education of teachers in a postmodern society, as the present period of modernization of education, undergoing reforms of different parties, makes higher demands on the professional competence of teachers is presented. Today it will be obvious that society needs a constantly learning mobile teacher and his improvement of personal and professional qualities. It is important to note that the training of a teacher who meets the requirements of today's professional activity is continuous, not continuous only during basic training, but also during continuing education courses and self-study, because the knowledge and skills acquired at university are no longer sufficient to solve new school problems.
Use of Google Translate for Translating Scientific Texts: An Investigation with Saudi English-Major Students

English is not the first language in Saudi Arabia, which makes most students face problems with the most used language globally, consequently pushing most Saudis to use the Google Translate (GT) application. This paper reports the attitudes and perceptions of Saudi EFL students on using Google Translate, the strategies applied, the number of editions they make, and the reasons for amending the Google Translate outputs. The study sample comprised 43 English major students at Qassim University, Saudi Arabia. A validated questionnaire was used for data collection, followed by translation tasks where the participants would do translations between English and Arabic languages. Results showed that most of the participants frequently used GT in their English learning. The results also indicated that Saudi EFL students frequently edited the syntax produced by GT in addition to checking the meaning of some new words. The study concludes with some recommendations, most importantly that Saudi EFL students should be motivated and encouraged to make use of GT to conserve their time; however, total dependency is not welcome.
A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train and test four prominent machine learning models, MNB, SVM, LogR, and RF, and an Ensemble of MNB, LSVC, and RF classifiers, on the labeled dataset. Our main findings suggest that machine learning may be used to classify emails based on their emotional content. However, some models outperform others. During the testing phase, we also discovered that the LogR and LSVC models performed the best, with an accuracy of 72%, while the MNB classifier performed the poorest. Furthermore, classification performance differed depending on whether the dataset was balanced or imbalanced. We conclude that machine learning models that employ emotions for email classification are a promising avenue that should be explored further.
A Critical Study on the Holistic and Multidisciplinary Approach of National Educational Policy 2020 (NEP 2020) in India

National Education Policy: 2020 is ground-breaking step taken by Indian Government in the field of Education. We are living in the post modern virtual world where the boundary of the education of a country is kaput and enriched with the affluent global thoughts. Indian education has the greatest heritage and glorious past. It has been altering its form according to the passage of time on the demand of the crisis influenced by social, political and economical issues. The present study will focus on the holistic and multidisciplinary dimension of New Education Policy: 2020. The researcher will discuss how holistic attitude of education plays an important role in the field of ‘all round development’ of the child; and the researcher will find out how much multidisciplinary approach is relevant in contemporary education system in the respect of global education. The researcher has gone through the report of the previous education policies, committees and studied related literature closely which are desirable for this study. The findings of this study will help its readers to understand the context to introduce NEP 2020 by Government; the reader will realize the stream of the change of education policy, he/she will aware previous history of education in brief. The researcher has taken philosophical research method to convey philosophy of education and philosophy of life as well. The novelty of this study is to recognize the importance of the approach of holistic education in modern education system and curriculum. And then the readers will be able to distinguish how much multidisciplinary educational approach is significant for survival which is the ultimate aim of all education.
Pelatihan Pengolahan Microsoft Excel untuk Membuat Bank Soal Bagi Guru SD Gugus V Lingsar

Utilization of technology, information and communication, especially the use of software such as Microsoft Word, Microsoft Power Point and Microsoft Excel is an ability that must be possessed by elementary school teachers in implementing an independent curriculum. The use of Microsoft Excel, for example, can assist teachers in processing various data, such as processing assessment instruments in the form of end-of-semester assessment questions for students. Based on interviews with the group leader V Lingsar, the results showed that teachers still needed time in making final semester assessment questions, starting from making question banks, sorting and randomizing questions into several question packages. To answer these problems, a service activity was carried out in the form of training on the use of Microsoft Excel to create a question bank. The training was attended by 20 teachers of the V Lingsar group. This activity consists of two sessions, namely the delivery of material and guided practice. All participants felt that the material could be understood well, guided practice could be carried out smoothly and this activity could be relevant to the needs of the teacher.
A Newly Hybrid Version of African Vultures Optimization Algorithm and Sine Cosine Algorithm for Mammographic Masses and its Diagnosis

Abstract From the last few periods, breast cancer is the foremost reason for cancer-related huge death rates in the midst of females. However, the recent study detection and diagnosis of these types of issues can successfully decrease the death rate. Masses are unique indicators of undetectable breast cancer noticeable in mammograms whose identification and judgment is a more complex or stimulating job due to its restrained natural surroundings. The seriousness of this issue is to motivate the scientists of different areas for their solution. With the great inspiration of this learning, in this work we are tried to present a new hybrid algorithm with the integration powerful features of two different algorithms such as African Vultures Optimization Algorithm (AVOA) and Sine Cosine Optimization Algorithm (SCA) and it is called AVSC method for identify the breast cancer. The features of the sine cosine algorithm helps to improve the exploitation and exploration phases of the basic AVOA method. This enhancement has been designed to improve the premature convergence, avoiding the local optima and fast convergence speed and many more. To verify the performance of the proposed scheme have been verified 29-CEC ’2017 test functions. Moreover, the AVSC method has been successfully applied on the two different mini-MIAS and DDSM databases including 68 and 500 mammograms, etc and attained a highest rate of sensitivity and false positives per image for the respective datasets. Hence, the tabulated results give the strong evidence that the AVSC is able to effectively resolve the highly complex tasks that others whole could be more helpful in biomedical sciences.
An End to End Encoder-Decoder Network with Multi-scale Feature Pulling for Detecting Local Changes From Video Scene

Local change detection for moving object detection is an essential step in any computer vision task. The most well-known technique is background subtraction BGS. However, the performance of BGS is strongly dependent on the background construction. The background construction to be robust in the presence of various challenges: dynamic backgrounds, illumination changes, camera jitter, etc. In this paper, we propose a novel encoder-decoder-based end-to-end deep learning framework for BGS. Thus, we explore a VGG-19 deep network with a transfer learning strategy as an encoder that deeply learned and extracted the features at different levels. We herewith propose a Multi-scale Feature Pulling MFP block which can retain the features at the various scales of the challenging video scenes. We also design a decoder network which is a stack of several transposed convolutional layers which precisely predict that each pixel of the target frame belongs to the background or foreground. The efficiency of the proposed algorithm is validated on the CDNet-2014 dataset by comparing its results against seventeen state-of-the-art techniques.
Pengembangan Media Pembelajaran Fisika Berbasis Game Edukasi: Analisis Bibliometrik Menggunakan Software VOSViewer (2017-2022)

Physics is one of the abstract subjects that require visualization so that learning media is needed so that students are able to understand the concepts studied. One of the learning media that displays visualization by utilizing technology in the form of educational games. This study aims to analyze the development of educational game learning media in physics learning with a bibliometric approach using VOSViewer software. The method used in this study is bibliometric analysis. There are 6 clusters of classification through VOSViewer software. The results of the study obtained articles used with the title "educational games" and "learning media", as well as the keyword "physics learning" began to increase rapidly in 2018, 2019, 2020, and 2021. The article was produced from various universities in Indonesia and uses Indonesian. In this study, 4 clusters were obtained that discussed the topic of developing educational game learning media in physics learning. For the topic of developing educational game learning media, it has been quite widely done, but for the topic of the effectiveness of the use of educational games in physics learning, it is still rare for a study to be carried out.Keywords: educational games, learning media, physics learning, bibliometric, vosviewer.
3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection

Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the large domain gap between the two sensors in terms of coordinates and data distribution when fusing their features. In this paper, we propose a novel camera-LiDAR fusion architecture called, 3D Dual-Fusion, which is designed to mitigate the gap between the feature representations of camera and LiDAR data. The proposed method fuses the features of the camera-view and 3D voxel-view domain and models their interactions through deformable attention. We redesign the transformer fusion encoder to aggregate the information from the two domains. Two major changes include 1) dual query-based deformable attention to fuse the dual-domain features interactively and 2) 3D local self-attention to encode the voxel-domain queries prior to dual-query decoding. The results of an experimental evaluation show that the proposed camera-LiDAR fusion architecture achieved competitive performance on the KITTI and nuScenes datasets, with state-of-the-art performances in some 3D object detection benchmarks categories.
Classification by Principal Component Regression in the Real and Hypercomplex Domains

Abstract Linear regression is a simple and widely used machine learning algorithm. It is a statistical approach for modeling the relationship between a scalar variable and one or more variables. In this paper, a classification by principal component regression (CbPCR) strategy is proposed. This strategy depends on performing regression of each data class in terms of its principal components. This CbPCR formulation leads to a new formulation of the Linear Regression Classification (LRC) problem that preserves the key information of the data classes while providing more compact closed-form solutions. For the sake of image classification, this strategy is also extended to the 4D hypercomplex domains to take into account the color information of the image. Quaternion and reduced biquaternion CbPCR strategies are proposed by representing each channel of the color image as one of the imaginary parts of a quaternion or reduced biquaternion number. Experiments on two color face recognition benchmark databases show that the proposed methods achieve better accuracies by a margin of about 3% over the original LRC and like methods.
Retracted: Relationship between Artificial Intelligence-Based General Anesthetics and Postoperative Cognitive Dysfunction

[This retracts the article DOI: 10.1155/2021/5553029.].
Effectiveness of Mobile Learning in Enhancing Saudi English Majors’ Skills in Learning Vowels

Learning Phonetics, particularly the transcription of the vowel sounds, is one of the challenges that non-native English majors face. It can be best learned using images and videos as supplementary materials in the phonetics class in addition to the teacher’s live demonstration. Mobile pronunciation applications encompass all these features and provide an affordable and accessible resource for learning. The present study investigates the effect of using a pronunciation application on non-native Saudi students majoring in English at Majmaah University. The study provides insights on how mobile learning can be an effective tool for both English instructors and learners in teaching and learning vowels. A Pretest–posttest design was adopted in the present case study and descriptive and inferential statistical analyses were applied. The results indicated that short vowels are more challenging to the learners than long vowels and diphthongs. Findings also revealed that there is a significant difference between the learners’ performance before and after using the application which indicated the positive effect of using it in developing non-native learners’ skills in learning Phonetics.
Quantum computing – the photonic approach

Abstract More than thirty billion dollars has gone into funding quantum technologies, and much of this money has been allocated to quantum computing projects. Accordingly, a large number of different realizations for quantum computing are being developed. The photonic approach is one of the most promising of these ideas because it is based on well‐known materials, established production processes, and operation at room temperature.
Resume Parser using Natural Language Processing

Manual extraction of information from the resume is very difficult and time taking process. The project mainly focuses on extracting the required information from the resumes using Natural Language Processing Techniques. The large set of resumes can be parsed using this kind of system. This project helps us know about the needed and important aspects while shortlisting the candidates and also helps the companies and high-level firms to select the quality employees from the extracted information. This project also helps to improve the shortlisting the process.
Continuous Proteretic Hopfield Neural Network in Walsh-based Distributed Storage

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a data set through a process that mimics the operations of a human brain. It may be used in many real-life applications such as speech and voice recognition, eCommerce, cybersecurity, etc. The activation function is one of the vital parts of neural networks, which affects the performance of neural network applications. Moreover, the choice of the activation function depends on the type of problem we want to solve. This paper presents a specific neuron activation function based on a property found in physical systems, "proteretic". This prosperity is applied to the Walsh-based distributed memory. We show that using the proteretic function with the Walsh-based memory enhances the storage performance by making the network converge faster than the other neural network activation functions.
Identification and Classsification Cyber Bullying among University’s Students

The development of technology not only has a positive impact but also has a negative impact. One of them is the shift in bullying trends, from traditional bullying to cyberbullying. Based on a report, as many as 210.3 million internet users aged 13-17 years ranked third in using social media. Cyberbullying victims in Indonesia reach 41-50% and 80% of teenagers become victims of cyberbullying. This study aims to determine the incidence of cyberbullying in students. Using an analytical survey research method with a cross-sectional survey design. Data collection through Cyberbullying and Online Aggression Survey questionnaires and data processing is presented in the form of a frequency distribution. The results showed that 125 respondents (75%) who experienced cyberbullying with types of cyberbullying included flaming 32,14%; exclusion 62,75%; harassment 47,44%; flooding 50%; masquerade log in 42,34%; trolling 25,52%; denigration 19,89%; outing 25%; dan sexual harassment 15,3%. The incidence of cyberbullying in adolescents is still high and all types of cyberbullying have been experienced by adolescents.
Intrinsic Image Transfer for Illumination Manipulation

This article presents a novel intrinsic image transfer (IIT) algorithm for image illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework composed of illumination, reflectance and content photo-realistic losses, respectively. Each loss is first defined on the corresponding sub-layers factorized by an intrinsic image decomposition and then reduced under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. We illustrate that all losses, with the aid of an "exemplar" image, can be directly defined on images without the necessity of taking an intrinsic image decomposition, thereby giving a closed-form solution to image illumination manipulation. We also demonstrate its versatility and benefits to several illumination-related tasks: illumination compensation, image enhancement and tone mapping, and high dynamic range (HDR) image compression, and show their high-quality results on natural image datasets.
An Analysis of Priorities in Developing Virtual Reality Programs for Core Nursing Skills: Cross-sectional Descriptive Study Using the Borich Needs Assessment Model and Locus for Focus Model

There are limitations to conducting face-to-face classes following the recent COVID-19 pandemic. Web-based education is no longer a temporary form of teaching and learning during unusual events, such as pandemics, but has proven to be necessary to uphold in parallel with offline education in the future. Therefore, it is necessary to scientifically organize the priorities of a learner needs analysis by systematically and rationally investigating and analyzing the needs of learners for the development of virtual reality (VR) programs for core nursing skills (CNS).This study aimed to identify the priorities of learners' needs for the development of VR programs for CNS using the Locus for Focus Model and Borich need assessment model.The participants included nursing students in South Korea who were in their second year or higher and had taken courses in fundamental nursing or CNS-related classes. The survey took place from May 20 to June 25, 2021. A total of 337 completed questionnaires were collected. Of these, 222 were used to conduct the final analysis. The self-report questionnaire consisted of 3 parts: perception of VR programs, demand for developing VR programs, and general characteristics. The general characteristics of the participants were analyzed using descriptive statistics. To determine the priority of the demand for developing VR programs for CNS, the Locus for Focus Model and the Borich priority formula were used.In all, 7 skills were identified as being of the top priority for development, including intramuscular injection, intradermal injection, tube feeding, enema, postoperative care, supplying oxygen via nasal cannula, and endotracheal suction.The analysis showed that nursing students generally needed and prioritized the development of VR programs for the nursing skills involving invasive procedures. The results of this study are intended to help in various practical education classes using VR programs in nursing departments, which are currently facing difficulties in teaching CNS on the web owing to COVID-19.