Reviewerly/exHarmony huggingface dataset:
Implementation of Smart Election System using IoT

In Indian Democracy, Voting is the essential fundamentals of each citizens. Building a secure balloting system has been a challenge for a due course of time. In this modern scenario, vote casting system has a few troubles with it. One of the most important problems in existing vote casting system EVM (Electronic Voting Machine), is confidentiality of voter, loss of secrecy of ballot, voter anonymity etc. In this paper, we proposed a biometric method which will resolve the problem of the modern-day voting system. Here, we added the concept of getting the fingerprint impact of a voter which is entered as input to the system. Then, compared with the collected information in the database. If the precise pattern suits with the reachable record, then casting a vote is permitted. Then the quick result is on the spot and counting is performed by means of IoT.
Design and Implementation of a Multichannel Convolutional Neural Network for Hate Speech Detection in Social Networks

As the number of social media comments available online grows, the spread of hate speech has grown gradually. When someone uses hate speech as a weapon to injure, degrade, and humiliate others, their freedom, dignity, and personhood can be jeopardized. Deep neural network-based hate speech detection models, such as the conventional single channel convolutional neural network (SC-CNN), have recently demonstrated promising results. The success of the models, however, is dependent on the type of language they are trained on and the training data size. Even with a small amount of training data, the model's performance can be improved by using a multichannel convolutional neural network (MC-CNN) model. The study assesses and compares the performance of a multichannel convolutional neural network model to single channel convolutional neural network models using a support vector machine (SVM) as a baseline. The models' F1 score values are computed, and promising results are obtained. The MC-CNN model outperforms the SC-CNN models in all three hate speech datasets. The study's findings indicate that the proposed MC-CNN model could be used as a deep learning-based alternative for hate speech detection.
Missing Person Identification System

One main objective of the project is to develop a missing youngster (people) realize application that may be useful for the folk whose beloved ones are kidnapped lost, or saved by the folks. In this Finding Missing folk's exploitation website we offer person with entire information base of the missing youngsters, in order that the police will track regarding the whereabouts or file a case relating the difficulty. And the youngsters found (known or unknown) details are uploaded by the police or by AN appointed agent. Entering details of the kids like height, age, etc. could be a protracted method and also the search output. Being out of touch with a loved one is concerning and not hearing from someone you care about is terrifying. Several cases of missing people have been reported for many years, where most of the searches turn out unsuccessful. In order to quickly reunite families and friends with their missing loved ones, a solution for effectively searching for the missing people is presented. Persons more easily and quickly. Missing person identification and tracking for intelligent video surveillance systems. Due to some reason the people leave the home or some child or old man’s forget the route of home to this missing case entry is updated in police station by using CCTV camera technology compare the each person with the available database and find these people. To improve this system concept system is designed. In this system designed to find the missing people. If the missing person found in the CCTV Video streaming then track the location of missing person. After missing person found in the CCTV Video streaming then send location SMS to relatives of missing person and Police station. So our system can perform the very important role in security and authentication issues. The user performs the main role in the system. Firstly he can register in the system, after the registration he can login to the system. User also adds the missing person details in the system. If missing person found in video then user can send SMS to the police station and also to person’s relatives. Here the admin perform the all administrative role in this system. Admin can add the user, remove the user etc. After getting the user’s result admin can view those details.
Blockchain: The Driving Force behind the World's Post-COVID-19 Economy

Blockchain was once one of the most talked-about breakthrough technologies because of its association with the famous currency Bitcoin and its potential applications in industrial fields. The COVID-19 pandemic has clearly shown the role of applying new technologies in disease prevention, public service provision, and business production by the state and businesses. However, currently, all technology applications have not met expectations, while many other shortcomings have occurred, such as energy consumption, scattered and unreliable data control, lack of legal basis, and others. The problem is how to maximize the application so that Blockchain becomes an effective tool to help build a fairer, more transparent, and sustainable world. This Blockchain will help bring great benefits through electronic data management, common data sharing, and mining rights, providing reliable information on a large scale, reducing production and business costs, and boosting the economic cycle. The article aims to clarify new insights about Blockchain and its benefits and limitations. The authors used descriptive statistics based on assessing the impact of the pandemic, analyzing opportunities and challenges, discussing the latest studies on the dynamics of Blockchain development after the pandemic. The novelty proposed policy implications to help businesses catch up and acquire success in restructuring the business technology system.
Machine Learning Algorithms for Labeling: Where and How They are Used?

With the increased availability of new and better computer processing units (CPUs) as well as graphical processing units (GPUs), the interest in statistical learning and deep learning algorithms for classification tasks has grown exponentially. These classification algorithms often require the presence of fully labeled instances during the training period for maximum classification accuracy. However, in industrial applications, data is commonly not fully labeled, which both reduces the prediction accuracy of the learning algorithms as well as increases the project cost to label the missing instances. The purpose of this paper is to survey the current state-of-the-art literature on machine learning algorithms that are used for assisted or automatic labeling and to understand where these are used. We performed a systematic mapping study and identified 52 primary studies relevant to our research. This paper provides three main contributions. First, we identify the existing machine learning algorithms for labeling and we present a taxonomy of these algorithms. Second, we identify the datasets that are used to evaluate the algorithms and we provide a mapping of the datasets based on the type of data and the application area. Third, we provide a process to support people in industry to optimally label their dataset. The results presented in this paper can be used by both researchers and practitioners aiming to improve the missing labels with the aid of machine algorithms or to select appropriate datasets to compare new state-of-the art algorithms in their respective application area.
Digital Authoritarianism

Surveillance practices are commonly used to control populations. Within the UAE, a system of total observation has been developed to target individuals and regulate social behaviors. This manifests in specific tools and undertakings through to structured regime ownership and legislation. A cohesive network of individuals has been established to oversee the collection and management of surveillance practices within the UAE, all possessing intimate and direct relations with Mohammed bin Zayed.
Automatic Reclassification of Volcano-Seismic Signals from Soufrière Hills Volcano, Montserrat, 1996-2008

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Automatic Reclassification of Volcano-Seismic Signals from Soufrière Hills Volcano, Montserrat, 1996-2008AuthorsGlennThompsoniDAlexisFalciniDJean-PhilippeMetaxianFrancoisBeauduceliDSee all authors Glenn ThompsoniDCorresponding Author• Submitting AuthorUniversity of South FloridaiDhttps://orcid.org/0000-0002-9173-0097view email addressThe email was not providedcopy email addressAlexis FalciniDInstitut de Physique du Globe de ParisiDhttps://orcid.org/0000-0002-9253-4901view email addressThe email was not providedcopy email addressJean-Philippe MetaxianIRD, IPGP, Université de Parisview email addressThe email was not providedcopy email addressFrancois BeauduceliDUniversité de ParisiDhttps://orcid.org/0000-0003-4803-6216view email addressThe email was not providedcopy email address
A study of Change Impact Analysis Techniques based on Requirement Defects during the Software Development Process

One of the major challenges faced by software companies is to produce high-quality software. Quality is not a state but a continuous process, it is critical to produce quality artefact at every stage of software development. Making modifications to software during and after development is a common occurrence. Executing the recommended modifications in software might have negative consequences such as lowering its quality or introducing new problems, which would raise the cost of software maintenance. As a result, a methodical approach to change management is necessary. Change Impact Analysis (CIA) is a method for controlling software modification that has already been implemented. CIA is becoming increasingly essential in various sectors, particularly software development. The objective of this paper is to do an analysis on CIA’s emerging elements, which evaluates the nature of every change request and seeks to integrate such changes into a program in a step-by-step manner. The paper aims to uncover the difficulties and challenges in CIA, current advancements, methodologies and tools in the field as well as the emerging scope of research in this field.
Research and implementation of a highly reliable transient and same source data comparison method based on fault recorder documents

Abstract The existing fault recording analysis system has powerful functions in the analysis of fault characteristic factors such as waveform analysis, vector analysis, and harmonic analysis. However, it uses redundant fault recording data to analyze, compare and locate faults in the power system. Rarely involved, far from reaching the level it should be, and even wasting fault recording data with large amounts of data and rich information content. A highly reliable transient homology data comparison based on recording files is given. Analytical method. Introduced the comparison method of the recording data from the same source, the content involves how to match the channel waveform of fault recorder documents, how to use the zero-crossing algorithm to align, how to find the starting point of the protection action through the mutation amount algorithm, and how to pass the cubic spline The interpolation algorithm obtains valid data, how to compare the valid data, and finally obtain the comparison result, and give advice on operation and maintenance.
Image quality and perception: introduction

This feature issue focuses on image quality and perception, including image and video quality, subjective and objective quality, and enhancement. The feature issue contains papers on several important topics, such as contrast discrimination, analysis of color imaging in cameras, image quality assessment, and more. The papers represent different important aspects in image quality and perception, contributing to the advancement of the field.
Overview of Green Cloud Computing

Cloud computing is a vital field of information and communication technologies , introducing new summons for environmental protection. Cloud computing technologies have a variety of application domains, since they offer scalability, are trustworthy and reliable, and offer high performance at relatively low cost. The cloud computing revolution is redesigning modern networking, and offering promising environmental protection prospects as well as economic and technological advantages. These technologies have the potential to improve energy efficiency and to reduce carbon footprints and (e-)waste. These features can transform cloud computing into green cloud computing. In this survey, we review the main achievements of green cloud computing. First, an overview of cloud computing is given. Then, recent studies and developments are summarized, and environmental issues are specifically addressed. Finally, future research directions and open problems regarding green cloud computing are presented. This survey is intended to serve as up-to-date guidance for research with respect to green cloud computing.
Deep Learning Based Phishing Websites Detection

Phishing is a crime that involves the theft of confidential user information. Those targeted by phishing websites include individuals, small businesses, cloud storage providers, and government organisations and websites. The majority of phishing prevention techniques involve hardware-based solutions, although software-based options are preferred due of cost and operational considerations. There is no answer to the problem of zero-day phishing assaults from the present phishing detection approaches since there is no solution to the problem. The Phishing Attack Detector based on Web Crawler, a three-phase attack detection system, was designed to handle these issues and accurately detect phishing incidences using a recurrent neural network in order to resolve these issues. It covers the input aspects of web traffic, web content, and Uniform Resource Locator (URL) based on the classification of phishing and non-phishing pages, as well as the output features of phishing and non-phishing pages.
Signature Verification Using a Convolutional Neural Network

Many studies have been conducted on Handwritten Signature Verification. Researchers have taken many different approaches to accurately identify valid signatures from skilled forgeries which resemble the correct signature. Unique characteristics from signatures can be accurately and rapidly analyzed with multiple layers of receptive fields and hidden layers. The main contribution of this method is to find and decrease fraud committed, especially in the banking industry.
Jarvis: AI Virtual Assistant

"Jarvis" was the protagonist of Tony's Stark starring in the movie Iron Man. Unlike the first jokes when Jarvis was Stark's servant, Jarvis' movie version is a smart computer that communicates critical issues, monitors his family, and helps build and organize his hero's suit. In this project, Jarvis is a Digital Health Assistant who uses social media i.e., voice that creates two-way communication between people and his personnel Computer, performing various tasks such as calling and chatting on WhatsApp, taking notes, locating, etc. In our project, we use the word just as communication means that Jarvis is a Speech recognition program. The concept of speech technology combines two technologies: Synthesizer and vision. Speech synthesizer captures input and generates audio streams as output this is achieved using the pyttsx3 library accompanied by Speech function. Speech recognition. On the other hand, he did the opposite. It treats audio streaming as input and thus converts it into text writing and this output uses the Speech Recognition library accompanied by the TakeCommand function. The word is a symbol of endless knowledge. Direct analysis and integration of complex voice signal is due to the vast amount of information contained in the signal. Therefore, digital signal processes such as Feature Visibility and Feature Matching are introduced to represent the voice signal. For this project, we are directly using a speech engine that uses an output element such as Mel scaled frequency cepstral. The mel scale frequency cepstral coefficients (MFCCs) obtained from the fourier transform and filter bank analysis are probably the most widely used in high-level speech recognition systems. We aim to create more jobs that can help people in their daily lives and reduce their efforts as in this project we have introduced Chrome and Youtube Automation. In our test, we checked that all of this functionality works well. We test this on 2 speakers (1 female and 1 male) to find the exact motive for the speculation.
On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets

Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observations, and choosing appropriate actions to execute in the environment to produce desired changes.We experiment with augmenting a transformer model for this task with modules that effectively utilize a wider field of view and learn to choose whether the next step requires a navigation or manipulation action.We observed that the proposed modules resulted in improved, and in fact state-of-the-art performance on an unseen validation set of a popular benchmark dataset, ALFRED.However, our best model selected using the unseen validation set underperforms on the unseen test split of ALFRED, indicating that performance on the unseen validation set may not in itself be a sufficient indicator of whether model improvements generalize to unseen test sets.We highlight this result as we believe it may be a wider phenomenon in machine learning tasks but primarily noticeable only in benchmarks that limit evaluations on test splits, and highlights the need to modify benchmark design to better account for variance in model performance.
Web Application for Quiz Monitoring System using NODE.JS and FIREBASE

The objective of the Quiz Web Application is to minimize the manual work of the examiner and make it easy for them to conduct an online exam. The target users will be staffs and the students. This project will not only overcome the manual system but also help the staff to track who attended their quiz. It will provide a dashboard for the examiner so that they can keep track on their quiz. The proposed system provides features to create quiz by authenticated user, automatic evaluation of the response and dashboard for monitoring the quiz.
A Survey on Reversible Image Steganography in Telemedicine

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Risk management model for quality assuring of education at the university

Introduction. The actual internal and external context of the university, in particular the COVID-19 pandemic, creates generates risks for the quality of education at the university. Risk management should be carried out systematically to reduce the probability of occurrence and negative consequences of risks. The purpose of the research is to develop and implement a risk management model involving systemic actions with risks for education at the university and to study the impact of the COVID-19 pandemic on these risks. Methods. Identification of possible risks for quality of education was carried out for the Russian Technological University (Russia). The risk analysis was carried out by written survey with fixing the potential causes and consequences of the risk. The survey involved teachers of all institutes (8) and departments of the university (about 100). The number of participants was more than 2000 people including heads of institutes and departments. The risk method is the analysis of the types, consequences and criticality of failures – FMECA. Research results. The risk management model of the educational process allows organizing the management of internal and external risks for university is presented. The data obtained showed that "poor school preparation in mathematics for mastering the programs of the technological university" was recognized as an unacceptable risk (RPN=74.8). The risk of "full transition to distance learning technologies" was recognized as critical for the quality of the educational process of the university (RPN=40.8). The source of this risk is the existing epidemiological situation in the country and the world caused by the COVID-19 pandemic. Control actions to reduce this risk have been developed within the university: reducing the probability of a risk event and reducing the consequences. The remaining risks were moderate or insignificant, a decision was made to accept them, continue monitoring and analysis in subsequent periods. Conclusions. Proposed risk management model allows the university to identify risks that could potentially have a negative impact on the educational process, to analyze and evaluate them. Priority risks are eliminated or minimized ensures the university's resilience to the impact of internal and external factors for quality of education.
Disaster Mitigation Innovation: Lessons Learned at a Regional Disaster Management Agency in South Sulawesi

Natural disasters are becoming more common in Indonesia, and their severity is increasing. To meet the challenges of updating disaster mitigation and rehabilitation capabilities, more innovative or better technology is required. In an effort to mitigate and overcome disasters, an innovation known as Simpul Pena has been developed. Simpul Pena is an information system for the control and operational center for disaster management in the regional disaster management agency (BPBD) in Wajo Regency. This study applied a qualitative descriptive approach. Data were collected through interviews, observations and document review. Interviews were conducted with the Head of BPBD Wajo Regency and the Head of the Emergency and Logistics Division, while observations were carried out to gain an understanding of the process of implementing public service innovations at the BPBD Wajo Regency. The results of the study showed that the implementation of the Simpul Pena innovation was effective and there has been consistency in its application. The impact of Simpul Pena was measured using five factors that can contribute to the success of innovation: leadership, management/organization, risk management, human resources, and technology. Disaster information system services were carried out through Simpul Pena via access to WhatsApp, Facebook and Instagram, resulting in integrated, coordinated, efficient and effective disaster management, as well as a significant increase in community participation in disaster management. The role of leadership was the most influential factor in shaping an innovative culture and behavior so that the organization continued to improve in developing service innovations to meet the challenges faced.&#x0D; Keywords: public sector innovation, disaster mitigation, regional disaster management agency, Wajo Regency, South Sulawesi Province