Reviewerly/exHarmony huggingface dataset:
WEB-BASED TEACHER AND STUDENT DATA INFORMATION SYSTEM OF SDN 19 SUMBER MARGA TELANG

With reference to the background of the problem above, the formulation of the problem to be discussed is formulated, namely "How is the student data management information system at SDN 19 Sumber marga telang?". The method used in this research is a quantitative research method with a descriptive approach. Quantitative research method is one type of research whose specifications are systematic, well-planned and clearly structured from the beginning to the making of the research design. Based on the results of the Blackbox test, it can be concluded that the purpose of this research has been achieved and designed an information system for managing data for SDN Sumber marga telang students computerized using the PHP programming language and using the Mysql database which can make it easier for staff to manage data and obtain the required information. quality and can assist in the decision-making process
Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media

By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people’s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters’ opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter’s data. Mainly two types of experiments were conducted and compared; (i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55).
A Study of Deep Learning Algorithms in Sentiment Analysis of Diverse Domains

Analysis of sentiment and opinion mining is the space of examination that investigates considerations, discernments, appraisals, practices, and feelings of individuals from the composed language on products, administration associations, human issues, occasions, points, and different traits. The main agenda of this paper is to depict the sentiments of people using deep learning algorithms and tools by classification methodology in categorizing polarity into positive, negative, and neutral.
Digital Boolean Logic Equivalent Reversible Quantum Gates Design

Digital logic equivalent quantum gates for easy integration of quantum logic with existing classical counterparts are proposed in this work. The proposed quantum gates are used to develop algorithms to emulate classical logic operations using the matrix transfer function method. The number of ancilla qubits for developing quantum gates is reduced by introducing swap operation. The proposed basic classical gates are implemented and validated for their correctness on IBM quantum experience (IBM QE) by applying 1024, 2048, and 8096 input samples. Subsequently, several classical circuits are realized employing the proposed set of classical equivalent quantum gates, which are validated with a significant number of statistical measurement samples. The resultant probabilistic measurements are more than 72% in favor of all the desired outcome for both basic gates and classical circuits, whereas the cumulative probability of the top two expected qubit states is greater than 95%.
Distribution of Quantum Entanglement through Fiber with Co-Propagating Classical Data

A complete system for distributing quantum entangled signals over fiber based on commercially available equipment will be demonstrated. Measurements are collected and controlled from a single location using an embedded optical data link.
Fully Integrated Silicon Photonic Tensor Core for Next- Generation Applications

Here we present our architecture for Silicon Photonic Tensor Core, capable of responding to the needs of Neural Networks, Augmented and Virtual Reality applications. We present a novel version fully integrated, from lasers to photodetectors.
Chatbot Based Self-diagnosis for Disease Prediction using Machine Learning

Healthcare sector is shifting focus on improving services by adopting new technological approaches.This enriches the functional characteristics of diagnostic data.With the increase in need of Medical services and lack of availability of the resources, Healthcare Chatbot is an attempt to assist common people with primary health care by reducing the burden on medical frontline workers.The objective of this research work is to design a 24/7 available chatbot that answers common medical queries, predicts diseases based on the symptoms and radiology images provided, aids with medication/ precautionary measures that are to be followed.A chatbot can provide a customized one on one interaction through text or voice interface and gives reply using Artificial Intelligence.It responds differently to messages containing certain keywords and uses Machine Learning to adapt their responses to fit the situation.The healthcare chatbot handles a large number of requested queries at a time making it reliable to use.The chatbot responds to medical queries only to the best of its knowledge database.
Organisation of students’ independent work aimed at developing their publication activities using project and scientific research methods

The aim of the research is to substantiate the necessity of organising first-year students’ independent work aimed at developing their publication activities when studying Humanities. In the paper, the author describes her own experience of organising the publication activities of the first-year students of the Institute of Telecommunications, training programme 11.03.02 Infocommunication technologies and communication systems (educational programme specialisation “Communication networks and switching systems”) as a form of the students’ independent work as exemplified by teaching “Russian language and the basics of business communication”. The scientific novelty of the research lies in characterising the ways of organising students’ independent work aimed at developing their publication activities based on the joint use of scientific research and project methods; in demonstrating the possibility of combining the educational format involving lectures and seminars and the independent work of the largest possible number of students aimed at developing their publication activities. As a result, the necessity of combining scientific research and project methods in organising students’ independent work aimed at developing their publication activities when studying the discipline “Russian language and the basics of business communication” has been substantiated; the skills and abilities the formation of which is mandatory for each student involved in publication activities have been identified and the competencies that are necessary for students who plan to conduct active publication activities in the future have been identified.
Quantum battery based on dipole-dipole interaction and external driving field

The Dicke model is a fundamental model in quantum optics, which describes the interaction between quantum cavity field and a large ensemble of two-level atoms. In this work, we propose an efficient charging quantum battery achieved by considering an extension Dicke model with dipole-dipole interaction and an external driving field. We focus on the influence of the atomic interaction and the driving field on the performance of the quantum battery during the charging process and find that the maximum stored energy exhibits a critical phenomenon. The maximum stored energy and maximum charging power are investigated by varying the number of atoms. When the coupling between atoms and cavity is not very strong, compared to the Dicke quantum battery, such quantum battery can achieve more stable and faster charging. In addition, the maximum charging power approximately satisfies a superlinear scaling relation P_{max}∝βN^{α}, where the quantum advantage α=1.6 can be reached via optimizing the parameters.
WEB 3.0 DECENTRALIZED CROWDFUNDING APP USING BLOCKCHAIN

Crowdfunding is a popular method for raising funds for projects, businesses, and social causes.However, traditional crowdfunding platforms are often centralized, opaque, and inefficient.In recent years, Blockchain technology has become a potentially effective remedy to these problems.By leveraging the benefits of blockchain, such as transparency, security, and efficiency, we can create a new paradigm for crowdfunding that is decentralized, transparent, and efficient.In this paper, we explore the use of blockchain in crowdfunding and describe a prototype crowdfunding platform that uses Solidity, Next.js,Node.js,Polygon, Metamask, IPFS, and Infura to provide a secure, efficient, and transparent way for users to create and donate to campaigns on the Ethereum blockchain.The use of smart contracts ensures that the funds are securely and efficiently allocated, while the integration of IPFS and Infura allows for decentralized storage of data and access to the Ethereum network.Our prototype demonstrates the potential of blockchain-based crowdfunding to transform the way we fund projects and causes.
Intelligent Platform Design for Intelligent Technology to Empower Martial Arts Training

This paper designs and implements an intelligent and scientific martial arts prescription intelligent system.The user scans the QR code on the official website of the smart coach with an Android phone or an Apple phone to download the mobile application APP, installs, registers and logs in, and then enters the mobile application APP.The main functions of intelligent training venues for basic personal information include fast feedback and multiple processing of sports video images, real-time acquisition and feedback of sports technology, quantitative physical fitness monitoring, non-invasive monitoring and evaluation of motor function, sports nutrition and trace element supplementation, post-training recovery system, and more.It can provide scientific data sources for researchers and coaches by monitoring the training environment and physiological state of athletes.At the same time, it can also provide support for the amount and intensity of sports training.Greatly promote the efficiency of Chinese martial arts teaching and competition.
The Art of Scientific Curation

Scientific content curation provides users across diverse disciplines and levels of experience with a valuable means of accessing relevant and reliable information amid the growing data landscape.
Entrepreneurship in pandemic times: impact of the COVID-19 pandemic on entrepreneurship rates in Brazil in 2020

The study aimed to analyze the impacts of the Covid-19 pandemic on entrepreneurship rates in Brazil in the year 2020. The methodology used was of the bibliographic type, characterized as exploratory, quantitative, and documental, with data from the research on entrepreneurship carried out by the Global Entrepreneurship Monitor, in partnership with SEBRAE and IBQP and the unemployment survey carried out by the IBGE from 2017 to 2020. The research showed that the Covid-19 pandemic reduced the rate of entrepreneurship and increased the rate of nascent entrepreneurs due to increased unemployment.
Hybrid Features Extraction for Emotion Detection using Deep Convolutional Neural Network

Abstract: These Human facial expressions communicate much information more visually than verbally. Recognition of facial expressions is essential for human-machine interaction. Applications for automatic facial expression recognition include but are not limited to, comprehending human behaviour, spotting mental illnesses, and creating artificial human emotions. Humancomputer interaction, autonomous cars, and a wide range of multimedia applications all rely heavily on facial expression detection. In this study, we present a reusable architecture for recognizing human emotions based on facial expressions. Two machine learning algorithms that may be learned in advance for use in real-time applications make up the framework. At first, we use AdaBoost cascade classifiers to find instances of faces in the photos. The extracted neighborhood difference characteristics then serve as a representation of a face's features according to its localized appearance data. Instead of only looking at intensity data, the NDF predicts multiple patterns depending on the connections between surrounding areas. Despite a high identification rate, facial expression recognition by computers remains difficult. Based on geometry and appearance, two widely used approaches for automated FER systems are often used in the literature. Pre-processing, face detection, feature extraction, and expression classification are the four steps that typically make up facial expression recognition. Various deep learning techniques (convolutional neural networks) were used in this effort to recognize the primary seven human emotions: anger, fear, happiness, surprise, and neutrality
PERFORMANCE AND ACTIVITY HANDLING DIGITALIZATION USING ERP SYSTEM WITH CLOUD DEPLOYMENT

This research paper explores the performance and activity handling aspects of digitalized Enterprise Resource Planning (ERP) systems deployed in the cloud.The study aims to investigate the consequences and benefits of incorporating digitalization and cloud deployment into ERP systems, as well as propose solutions for unlocking their potential using advanced technologies and modern solutions.The research employed a mixed-methods approach, combining a case study analysis and a survey, to gather data from organizations that had implemented digitalized ERP systems with cloud deployment.The analysis of the collected data revealed that digitalization and cloud deployment enhance project management capabilities, improve system performance and user experience, streamline activities, and promote effective collaboration among team members.The findings highlight the benefits of digitalization and cloud deployment, such as improved efficiency, enhanced coordination, and better decision-making.The research provides insights for organizations considering digitalizing their ERP systems and deploying them in the cloud, while acknowledging factors such as internet connectivity, data security, and user interface design.The limitations of the research include the availability of a diverse sample and the specific nature of the evaluated ERP system.In conclusion, the study successfully achieves its objectives, reviews existing literature, presents implementation details, and discusses the implications of the findings, contributing to the understanding of ERP systems, digitalization, and cloud deployment.
Offline Signature Verification Using Local Radon Transform And NNMachines

To reduce noise from the photos, preprocessing is done initially.The denoised photos are then used to extract features.From all of the test photos, we extract the characteristics for this paper.The test image's characteristics are then likewise extracted.The classifier is then given the retrieved features from the training and test pictures.The classifier compares the feature values to the actual label before producing the classification outcome.We provide a unique method for classifying colour images based on content using NN.One of the reasons for the large dimensionality of feature space is that traditional classification algorithms perform badly on content-based picture classification tasks.In this study, characteristics that may be derived from histograms of colour components are used to classify colour images.Better efficiency and insensitivity to minute changes un the camera's perspective, such as translation and rotation, are two advantages of employing colour picture histograms.
Pose Invariant Face Recognition Using Markoy Random Fields Umang Garg

How to manage position differences between the probing and gallery face photos is one of the important issues facing current face recognition algorithms.In this study, I demonstrate how to use Markov Random Fields (MRFs) and a productive variation of the belief propagation technique to rebuild virtual frontal view from a given facial picture.Suggested technique divides the input face picture in an arrangement of overlapped patches, which are then combined at the frontal view using an estimate of a worldwide optimum set of local warps.The alignment of every patch with pictures from a training library of frontal faces yields a list of potential warps for that patch.Then, using an MRF, the issue of locating ideal warps is developed as a discrete labelling problem.Any face recognition method may then be used to the rebuilt frontal face picture.Our technique has two key benefits: it requires manually choosing which face landmarks to estimate.I also describe an approach for detecting whether or not a particular face image is at a frontal or non-frontal position, which will help our pose normalisation method perform better on face images.Experimental findings on several datasets are provided to show how well the suggested approach works when implemented in MATLAB.
BLOCK PRINTING TECHNIQUE AND IT’S USED IN BAGH PRINT

Block printing is a technique where fabrics are colored and colored using wooden blocks.This page provides a primer on block printing and describes the Bagh block print, an iconic piece of Indian textile art that has gained recognition throughout the world.Bagh print is known for its natural color hand block printing on a wide range of natural materials.Although 33% of respondents classified it as both a handcraft and a handloom, it is mostly a handicraft.Bagh Prints are fabrics that are hand-blocked using solely natural colors.Bagh print fabric is a popular example of the Textile printing industry; it has geometric and floral designs that repeat in red and black on a white backdrop.The world-famous art form known as Bagh print was developed in the Indian state of Madhya Pradesh.
Time and Space Complexity Reduction of KFDA-based LTE Modulation Classification

Background: Kernel Fisher discriminant analysis (KFDA) is a nonlinear discrimination technique for improving automatic modulation classification (AMC) accuracy. Our study showed that the higher-order cumulants (HOCs) of the Long-term evolution (LTE) modulation types are nonlinearly separable, so the KFDA technique is a good solution for its modulation classification problem. Still, research papers showed that the KFDA suffers from high time and space computational complexity. Some studies concentrated on reducing the KFDA time complexity while preserving the AMC performance accuracy by finding faster calculation techniques, but unfortunately, they couldn't reduce the space complexity. Objective: This study aims to reduce the time and space computational complexity of the KFDA algorithm while preserving the AMC performance accuracy. Methods: Two new time and space complexity reduction algorithms have been proposed. The first algorithm is the most discriminative dataset points (MDDP) algorithm, while the second is the k-nearest neighbors-based clustering (KNN-C) algorithm Results: The simulation results show that these algorithms could reduce the time and space complexities, but their complexity reduction is a function of signal-to-noise ratio (SNR) values. On the other hand, the KNN-C-based KFDA algorithm has less complexity than the MDDP-based KFDA algorithm Conclusion: The time and space computation complexity of the KFDA could be effectively reduced using MDDP and KNN-C algorithms; as a result, its calculation became much faster and had less storage size.
SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners

Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely used for students learning English as a Second Language (ESL). In this paper, we present a large-scale SC dataset, SC-Ques, which is made up of 289,148 ESL SC questions from real-world standardized English examinations. Furthermore, we build a comprehensive benchmark of automatically solving the SC questions by training the large-scale pre-trained language models on the proposed SC-Ques dataset. We conduct detailed analysis of the baseline models performance, limitations and trade-offs. The data and our code are available for research purposes from: https://github.com/ai4ed/SC-Ques .
Utilizing Artificial Bee Colony Algorithm as Feature Selection Method in Arabic Text Classification

A huge amount of crucial information is contained in documents. The vast increase in the number of E-documents available for user access makes the utilization of automated text classification essential. Classifying or arranging documents into predefined groups is called Text classification. Feature selection (FS) is needed for minimizing the dimensionality of high-dimensional data and extracting only the features that are most pertinent to a particular task. One of the widely used algorithms for feature selection in text classification is the Evolutionary algorithm. In this paper, the filter method chi-square and the Artificial Bee Colony (ABC) algorithm were both used as FS methods. The chi-square method is a useful technique for reducing the number of features and removing those that are superfluous or redundant. The ABC technique considers the chi-square method's chosen features as viable solutions (food sources). The ABC algorithm searches for the most efficient selection of features that increase classification performance. Support Vector Machine and Naïve Bayes classifiers were used as a fitness function for the ABC algorithm. The experiment results demonstrated that the proposed feature selection method was able of decreasing the number of features by approximately 89.5%, and 94%, respectively when NB and SVM were used as fitness functions in comparison to the original dataset, while also enhancing classification performance
Strategi Guru dalam Upaya Meningkatkan Minat Baca dengan Media Video di Kelas 3 MI NU Matholiul Falah dalam Pembelajaran Bahasa Indonesia

This article contains strategies to increase reading interest in third grade students of MI NU Matholiul Falah and teachers' strategies in the learning process in 2021/2022. This type of research is a qualitative research with a descriptive approach. This study aims to increase students' reading interest by using video media and singing methods. This classroom action research was carried out in 1 cycle with four stages, namely: planning, implementation, observation, reflection. The object of this research is the third grade students of MI NU Matholiul Falah in the Japanese village, Mejobo, Kudus, totaling 29 students. The research instruments used were observation sheets, interview guidelines, questionnaire sheets, and documentation. The results of the analysis based on observations, interviews, and questionnaires can be obtained that the reading interest of the third grade students of MI NU Matholiul Falah is low. Factors that influence this are the existence of internal and external factors. The results showed that the use of video media and singing methods in Indonesian language learning materials could increase students' reading interest which was marked by an increase in completeness in 1 cycle (87%) with an average value of 89,85.