Reviewerly/exHarmony huggingface dataset:
Forecasting Identity Theft Victims: Analyzing Characteristics and Preventive Actions through Machine Learning Approaches

Researchers in criminology and criminal justice have been making increasing use of the machine learning approach to investigate questions involving large amounts of digital data. We make use here of survey data on over 220,000 respondents drawn from three waves of the National Crime Victimization Survey Identity Theft Supplement (NCVS-ITS) conducted by the Bureau of Justice Statistics (BJS) in 2012, 2014, and in 2016. We use three distinct machine learning algorithms to analyze these data: 1) logistic regression; 2) decision tree; and, 3) random forest. We assess the efficacy of these approaches against these evaluative criteria: the overall percentage of correct classification, receiver operating characteristics (ROC), the area under the ROC curve (AUC), and feature criticality. Our findings indicate that the logistic regression algorithm performs best in predicting overall identity theft victimization, misuse of credit cards, misuse of financial accounts of other types, and the opening of new accounts; the random forest algorithm performs best in predicting misuse of checking/saving accounts. Our findings suggest that the respondent’s age, educational level, and online shopping frequency are significantly related to identity theft victimization. Additionally, frequently checking credit reports and changing passwords of financial accounts are strong predictors of identity theft victimization. We draw out the implications of our work for our collective understanding of identity theft, and for informing our judgment as to the potential utility of the use of machine learning approaches in criminology and criminal justice.
YOUTUBE ADVIEW PREDICTION USING REGRESSION MODEL

The main goal is to create a machine learning regression that can estimate the number of YouTube adviews based on other parameters. Advertisers on YouTube pay content creators based on how many times their ads are viewed and clicked. They want to estimate the adview based on other metrics like vidid, adviews, published, duration, views, comments, likes etc. CSV files are utilised for training and fitting, and then they are tested to get the best outcomes. As a result, the goal of the project is to train multiple regression models and select the best one for predicting the number of adviews. We validate datasets and packages like Numpy, Pandas, and Sklearn for their form and data type. Also, we visualise and clean the dataset followed by transforming attributes into numerical values. Using different regression algorithms, normalise the data and separate it into training and test sets. The model is saved and used to predict on the test set. To acquire better outcomes, data or information must be improved, filtered, and cleansed before being fed in based on numerous criteria.
Dual-Network Connectionist Modelling of Development

This work is the modelling complement to empirical studies which have been carried out at the University of Hertfordshire into the theory of Representational Redescription (RR; Karmiloff-Smith, 1992). This important theory is currently central to psychological theories of development and has been described using a balance beam task (Karmiloff-Smith, 1992). This task is representative of a whole class of early learning skill problems but has not been modelled before. In a new approach to modelling cognitive development a method proposed for solving reinforcement learning tasks, reward-driven learning (Gullapalli, 1995), is applied.
Hubungan Motivasi Belajar dengan Konsentrasi Belajar pada Mahasiswa Bahasa Jepang Universitas Riau (Penelitian Korelasi pada Mahasiswa Semester IV Prodi Pendidikan Bahasa Jepang di Universitas Riau)

Concentration is one of the things that can affect the learning process. Concentration in learning is generally related to the motivation of students. If the motivation is good, then the correlation with the interest and concentration of students' learning will increase. This research is a correlational research with a quantitative approach. The data in this study were obtained by providing two types of instruments, namely a learning motivation test and a concentration test. The results of this study indicate that there is a positive influence between learning motivation and learning concentration of students of the Japanese Language Education Program at the Universitas Riau in the 2021/2022 academic year of 0.619 with a moderate or adequate category. This shows that the better the student's learning motivation, the better the student's learning concentration will be.
DETECTING UNAUTHORIZED OR FRAUD PROFILES USING ARTIFICIAL NEURAL NETWORKS

In moment's digital age, the ever- adding reliance on computer technology has left the average citizen vulnerable to crimes similar as data breaches and possible identity theft. These attacks can do without notice and frequently without announcement to the victims of a data breach. At this time, there's little provocation for social networks to ameliorate their data security. These breaches frequently target social media networks similar as Face book, Twitter, Instagram and many other platforms. They can also target banks and other fiscal institutions. Vicious users’ produce fake accounts to phish login information from unknowing users. A fake profile will shoot friend requests to numerous users’ with public profiles. These fraud account users bait unknowing genuine users with film land of people that are considered seductive. Once the person accepts the request, the fake user of the phony profile will spam friend requests to anyone this person is a friend. Then, by using Artificial Neural Networks (ANN) we're relating whether given account details are from genuine or fake users’. ANN algorithm will be trained with all former users’ fake and genuine account data and also whenever we gave a new test data then that ANN train model will be applied on new test data to identify whether given new account details are from genuine or fake accounts. Online social networks similar as Face book or Twitter contain users’ details and some vicious users’ will hack social network database to steal or transgress genuine user’s information, to cover users’ data we're using ANN Algorithm. To train ANN algorithm we're using below details from social networks. Age of Account, User gender, Age of user, Profile link description, Usage count, Friends or followers count, Position, Location IP address, Status.
Fake News Detection using Machine Learning

Abstract: This Project comes up with the applications of NLP (Natural Language Processing) techniques for detecting the 'fake news', that is, misleading news stories that comes from the non-reputable sources. Only by building a model based on a count vectorizer (using word tallies) or a (Term Frequency Inverse Document Frequency) tfidf matrix, (word tallies relative to how often they’re used in other articles in your dataset) can only get you so far. But these models do not consider the important qualities like word ordering and context. It is very possible that two articles that are similar in their word count will be completely different in their meaning. The data science community has responded by taking actions against the problem. There is a Kaggle competition called as the “Fake News Challenge” and Facebook is employing AI to filter fake news stories out of users’ feeds. Combatting the fake news is a classic text classification project with a straight forward proposition. Is it possible for you to build a model that can differentiate between “Real “news and “Fake” news? So a proposed work on assembling a dataset of both fake and real news and employ a Naive Bayes classifier in order to create a model to classify an article into fake or real based on its words and phrases
The Adaptive PLIC-VOF Method with Overset Meshes

Piecewise Linear Interface Calculation (PLIC) schemes have been extensively employed in the Volume of Fluid (VOF) method to capture the interface between water and air in marine applications.The Adaptive Mesh Refinement (AMR) is often adopted to increase the local mesh resolution dynamically within the cells which are close to or contain the interface.Dynamic overset meshes can be especially useful in applications involving component motions involving ship/offshore platform hydrodynamics, semi-submerged propellers, water entry/exiting, etc.An adaptive PLIC-VOF method with overset mesh for static objects has been introduced in the present study.Simulations are performed under the framework of OpenFOAM with a modified flow solver.Numerical simulations of two dam-breaking problems with overset meshes and adaptive PLIC-VOF method have been successfully performed.An extension of solid body movement supporting is currently ongoing.
EVALUASI PEMBELAJARAN ONLINE MATEMATIKA SISWA KELAS 4 SD NEGERI KEDUNGPEDARINGAN

Mathematics learning is currently less efficient in the pandemic season because it reduces all face-to-face activities between teachers, students and their friends. This study aims to determine the ease and constraints of online learning mathematics for grade 4 SD Negeri Kedung Pedaringan. This type of research was descriptive qualitative. Data collection in the form of interviews and questionnaires containing questions to determine effectiveness during online learning activities during the covid 19 pandemic. The results of research through interviews and questionnaires stated that online learning without face-to-face was transferred to internet access and social media in the form of WhatsApp applications and google meet applications. Some of the conveniences of online learning were learning activities did not have to be at school, time and place were free as long as learning evaluation activities were known directly by the teachers. Some of the problems of online learning were students had difficulty of understanding the material, the teacher were not optimal in explaining the material in detail, not all the parents of students understand the material given by the teachers, unstable internet access has disrupted students' online learning.
A design in system architecture based on mobile cloud computing for a virtual try-on solution

Cloud computing is an emerging technology in this digital century. It provides an excellent but low-cost source for database storage, computing power, applications, and services through an internet-delivered cloud platform. Thanks to the cost savings in investing and maintaining physical data centers, as well as the stability of Quality of Service (QoS), it has no restrictions on company size or sector for enterprises to shift their operations to a cloud platform. The fashion industry, particularly the fashion e-commerce sector, is a case study in leveraging the cloud platform via a technology called “Virtual try-on” (VTO). VTO solution allows fashion brands to increase the shopping experience, however, requires installing and maintaining a bulky system for implementation. There are different methods and approaches to design architectures using cloud computing, however, there have not been many studies addressing tasks related to machine learning that uses the high Graphics Processing Unit (GPU) encountered in VTO solutions. To design a scheduler that could optimize the system performance while lowering operational expenses in VTO solutions, this research proposes a system to (1) handle synchronous model and asynchronous model separately and clearly, (2) perform multi-layered task processing architecture by hashing task ID and implementing a queue management system. This method would satisfy three major requirements: (1) Avoid complex hardware requirements for users, (2) Ensure the system stability and the ease of horizontal and vertical extension, and (3) Protect user information privacy.
Analysis Method for Teacher-Student Interaction in Online English Courses

In online learning, the teacher-student interaction mainly takes place in the form of themed discussion anytime, anywhere, and the posting and commenting on the theme. It is of great practical significance to select a proper behavior analysis method that effectively analyzes the mass data on teacher-student interaction. Taking online English courses as an example, this paper explores the teacher-student interaction during online learning. Firstly, the ant colony algorithm was adopted for cluster analysis of teacher-student interaction, and the analysis procedure was detailed. Next, the features of teacher-student interaction were illustrated from three aspects, namely, overall interaction on online learning platform, progress of themed discussion, and teacher-student interaction, in order to examine the teacher-student interaction model. Finally, the features were obtained for teacher-student interaction in themed discussion of online English courses.
Gesture Language Translator Using Raspberry Pi

Abstract: Sign language is a remarkable development that has evolved over time. Unfortunately, there are some disadvantages associated with this language. When conversing with a speech disabled person, not everyone understands how to interpret sign language. Without an interpreter, communication is difficult. We need a product that is both adaptable and robust. We need to transform sign language so that it can be understood by common people and the differently abled can communicate without hurdles. Keywords: RaspberryPi, Gestures ,Sign Language, Image Detection, Python
Ensure Data Protection in Cloud Computing using ASCII Based Encryption Technique

Cloud computing is an engineering discipline which is concerned with all aspect of storing and sharing useful data. Today, millions of users are able to upload and download useful information on cloud environment from anywhere at any time. Recently, many security issues are encountered in real time applications with a great loss of data and information. Subsequently, many research works have been done to highjack the security issues of cloud computing. Still, all feasible solutions may not applicable at all time and some practical situations. In this paper, we designed and implemented an ASCII based encryption technique that securely uploads, sharing the data on cloud framework with a minimum computational overhead and entropy. The proposed technique will increase the throughput of the system and it can be deployed on a cloud environment for sharing and securing useful data information.
Research on the construction of interactive hybrid teaching mode based on mobile intelligent link technology

In recent years, the network teaching platform of college classroom based on mobile intelligent connection technology has emerged, which has promoted the reform of education supply mode and teaching method. Based on the investigation of online learning tools and learning time of contemporary college students, this paper studies and establishes a new course teaching method suitable for application-oriented colleges in the Internet era. Relying on mobile intelligent learning platform, it combines interactive and mixed different teaching methods. This paper explores the reasonable construction of interactive and mixed teaching mode between intelligent teaching platform and offline teaching in professional courses in colleges and universities based on new media, cloud computing and big data technology, and puts forward personal opinions on the construction of interactive and mixed course mode in colleges and universities based on mobile intelligent connection technology.
PENERAPAN ALGORITMA NEAREST NEIGHBOR DALAM MEMPREDIKSI KELAYAKAN PENERIMAAN KARTU KREDIT PADA BANK CIMB NIAGA

A credit card application is a financial facility that allows a person or business entity to borrow money to buy a product and pay it back within a specified time. Credit card manufacture often experiences risk in providing credit, both moderate, good and very good risk, due to lack of data analysis and lack of attention. So the purpose of this study is to analyze the risks that often occur in making credit cards. And provide efficient solutions. In solving this problem, it is necessary to apply the method. The method used in this study is Nearest Neighbor. The Nearest Neighbor algorithm is an approach to finding cases by calculating the proximity between new cases (testing data) and old cases (training data), which is based on matching the weights of a number of existing features. Therefore, it can be applied in analyzing old customer data with data new so that comparisons can be made whether or not it is eligible to receive credit. The results obtained in this study are the results of testing systems and methods by producing a level of risk.
TEACHING LINEAR ALGEBRA TO STUDENTS OF ECONOMIC AND TECHNICAL SPECIALTIES: SIMILARITIES AND DIFFERENCES

Currently, training of students of any specialty at the university implies the formation of professional competencies."Linear algebra" is given particular place in this process.After studying this subject students should be able to use the acquired fundamental knowledge in their future professional activities.In addition, students should have the ability to determine general forms and patterns in linear algebra issues.An important role in the process of training specialists is played by the ability to mathematically correctly set science or economic problems, for this it is necessary to have a good understanding of the statements of classical mathematical problems in general.With a high-quality assimilation of the material and a sufficiently good ability to apply it in the required situation, students of technical specialties can plan and carry out pedagogical activities in the future, taking into account the specifics of the subject area in educational organizations.After studying at the university, specialists should be focused on both theoretical and applied activities.The increased requirements for the quality of graduate training are applied to both economic and technical specialties.In this regard, the modular approach to teaching the subject is gaining weight.The ability to develop modules of the same subject that are different in content and complexity for different specialties is of key importance in the training of technical and economic students.Students can immediately work out the practical application of the studied material on examples from their particular specialty, paying sufficient attention to the analysis of various specific nuances without spending too much time on analyzing general examples.The article deals with the issues related to teaching of "Linear algebra" to students of economic and technical specialties of the Chuvash State University, the similarities and differences in the presentation of the material.The main focus is on modern approaches in teaching the discipline and modular approach as well.
Akurasi Klasifikasi Pengguna terhadap Hotspot WiFi dengan Menggunakan Metode K-Nearest Neighbour

The Current wireless technology is used to find out where the user is in the room. Utilization of WiFi strength signal from the Access Point (AP) can provide information on the user position in a room. Alternative determination of the user's position in the room using WiFi Receive Signal Strength (RSS). This research was conducted by comparing the distance between users to 2 or more APs using the euclidean distance technique. The Euclidean distance technique is used as a distance calculator where there are two points in a 3-dimensional plane or space by measuring the length of the segment connecting two points. This technique is best for representing the distance between the users and the AP. The collection of RSS data uses the Fingerprinting technique. The RSS data was collected from 20 APs detected using the wifi analyzer application, from the results of the scanning, 709 RSS data were obtained. The RSS value is used as training data. K-Nearest Neighbor (K-NN) uses the Neighborhood Classification as the predictive value of the new test data so that K-NN can classify the closest distance from the new test data to the value of the existing training data. Based on the test results obtained an accuracy rate of 95% with K is 3. Based on the results of research that has been done that using the K-NN method obtained excellent results, with the highest accuracy rate of 95% with a minimum error value of 5%
Dokter Tunggu, A Hospital Application for Patient of Healthcare and Social Security Agency

PT. Zaps Technology is a company engaged in technology and information by producing application products with the name Dokter Tunggu (Doku). The application was created to eliminate queues that often occur in Healthcare and Social Security Agency patient services at level I Hospitals and Health Facilities. Place of company at Bekasi Jawa Barat, The location is said to be chosen because Bekasi is one of the supporting areas for the capital city and has a variety of complete business facilities. This company's strategy is to create innovations in Healthcare and Social Security Agency patient services where the application made has various features that are able to eliminate queues. The application has an online referral menu on the application so that Hospitals, Level I Facilities and Healthcare and Social Security Agency patients are easier to take advantage of BPJS services. The waiting doctor application will display real time conditions at the referral hospital so that BPJS users can monitor the condition of BPJS services at the destination Hospital. Keywords: Dokter Tunggu, Hospital, Online Sevice, Business Planning.
Approximation of Empirical and ANN Based Solar Radiation Models for Four Smart Cities in Tamil Nadu with Most Prompting Input Parameters

The abundant source of Solar Radiation is possible in different latitude of Tamil Nadu. The several Empirical models have developed and accuracy of the model is further validated with Artificial Neural Network models. The ANN and Empirical model is developed with different combination of input attributes. The Approximate Sunshine Solar Radiation, Temperature and Relative Humidity are the input attributes. In this paper, it has been evaluated that combination of Approximate Sunshine Solar radiation and Temperature ANN based model is best for prediction of Daily Global Solar Radiation when compared to ST based Empirical model. The Correlation analysis is performedfor selection of most relevant input attributes. And hence the estimation of daily global solar radiation is performed with minimum number of input attributes. The training and testing of each model is performed by ANN Simulink model. And from this trainingofinputvariables,theoverfittingofthemodelisreduced. The estimated GSR is validated with experimental data which is collected from Tamil Nadu AgricultureUniversity.
Proof of Golomb’s conjecture in Fq with Γ$-pseudorandom sequences

This article offers a short proof of Golomb's conjecture, and then our results show that the sequences are pseudorandom in $\mathbb{F}_2$.