Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Machine Learning Algorithms

Abstract: Credit Card Fraud can be defined as a case where a person uses someone else’s credit card for personal reasons while the owner and the card-issuing authorities are unawareof the fact that the card is being used. Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. In the era of digitalization, the need to identify credit card frauds is necessary. Fraud detection involves monitoring and analyzing the behaviour of various users to estimate, detect or avoid undesirable behaviour. To identify credit card fraud detection effectively, we need to understand the various technologies, algorithms and types involved in detecting credit card frauds. The algorithm can differentiate transactions which are fraudulent or not. To find fraud, we need to pass dataset and knowledge of the fraudulenttransaction. Algorithms analyze the dataset and classify all transactions. Fraud detection involves monitoring the activitiesof populations of users to estimate, perceive or avoidobjectionable behaviour, which consist of fraud, intrusion, and defaulting. Machine learning algorithms are employed to analyse all the authorized transactions and report the suspicious ones. We have taken an imbalanced dataset of transactions to detect the frauds
Approaching hate speech through behavioral education - suggested systemic perspective

The present study, exploratory in nature, aims to present an analysis of hate speech reasons through those for hate in general, and suggest a potential approach for dealing with hate speech from an early educational time, through a systemic perspective meant to change behaviors. We start from the premise that fake news and hate speech are closely intertwined, with mutual determinacy (hate speech influences fake news transmission and received fake news further stimulate hate speech) and many similar consequences in terms of confusion, lack of trust, inappropriate behaviors and crises’ sharpening effects. Once we know the reasons for which people hate, we are able to work on those causes, and stop or at least diminish the problem, and not just the symptoms. It is an exploratory study, with a literature review type analysis for hate, in general and hate speech in particular, followed by a pilot survey on a small sample of students and then a suggested educational behavioral approach for diminishing hate related behaviors. We suggest three disciplines for the educational content – intercultural management, interdisciplinary education and critical thinking – together with three system changes – collaborative, second-order type of change, rethinking of students’ and professors’ assessments and building of well-being frameworks.
Software quality model based on development team characteristics

&lt;span lang="EN-US"&gt;Many factors have a significant impact on producing high-quality software products. Development team members are among the most important factors. Paying attention to the quality from this perspective will be a good innovation in the software development industry. Given that team members play a very important role in software products, this study tries to focus specifically on team characteristics in software product quality and provide a qualitative model based on this. The required data were collected through observations and interviews with project managers and development team members in several companies under study. Then, data were analyzed through hierarchical analysis. According to the results, the use of this model led to the improvement of the software development process so that the team members were satisfied with it. Also, time management was improved, and the customer expressed his satisfaction with the use of this model. Finally, data analysis showed that this model may lead to faster product delivery.&lt;/span&gt;
Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition

&lt;p&gt;&lt;span lang="EN-US"&gt;In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015.&lt;/span&gt;&lt;/p&gt;
A Study on Lexical Chunks in Different Moves of Abstracts of Native and Chinese Applied Linguistic Journals

The norm for abstracts of research articles (RAs) is important and will win the RAs more chances of being received. The analysis of how abstracts are written in papers published in well-recognized international journals can serve as a reference to help writers conform to the norm. This paper, adopting Hyland&amp;rsquo;s classification and Santos&amp;rsquo; framework, employing a corpus-based method, compares how native and Chinese writers use lexical chunks differently in abstracts from a perspective of move analysis. The results show that Chinese writers tend to use more lexical chunks; there is no significant difference in the functional distribution on the whole but significant differences are found in two moves of &amp;ldquo;situating the research&amp;rdquo; and &amp;ldquo;discussing the research&amp;rdquo;.
A New Method for Image Protection Using Periodic Haar Piecewise-Linear Transform and Watermarking Technique

The paper presents a novel data-embedding method based on the Periodic Haar Piecewise-Linear (PHL) transform. The theoretical background behind the PHL transform concept is introduced. The proposed watermarking method assumes embedding hidden information in the PHL transform domain using the luminance channel of the original image. The watermark is embedded by modifying the coefficients with relatively low values. The proposed method was verified based on the measurement of the visual quality of an image with a watermark with respect to the length of the embedded information. In addition, the bit error rate (BER) is also considered for different sizes of a watermark. Furthermore, a method for the detection of image manipulation is presented. The elaborated technique seems to be suitable for applications in digital signal and image processing where high imperceptibility and low BER are required, and information security is of high importance. In particular, this method can be applied in systems where the sensitive data is transmitted or stored and needs to be protected appropriately (e.g., in medical image processing).
Novel Hybrid Fusion-Based Technique for Securing Medical Images

The security of images has gained great interest in modern communication systems. This is due to the massive critical applications that are based on images. Medical imaging is at the top of these applications. However, the rising number of heterogenous attacks push toward the development of securing algorithms and methods for imaging systems. To this end, this work considers developing a novel authentication, intellectual property protection, ownership, and security technique for imaging systems, mainly for medical imaging. The developed algorithm includes two security modules for safeguarding various picture kinds. The first unit is accomplished by applying watermarking authentication in the frequency domain. The singular value decomposition (SVD) is performed for the host image’s discrete cosine transform (DCT) coefficients. The singular values (S) are divided into 64 × 64 non-overlapping blocks, followed by embedding the watermark in each block to be robust to any attack. The second unit is made up of two encryption layers to provide double-layer security to the watermarked image. The double random phase encryption (DRPE) and chaotic encryption have been tested and examined in the encryption unit. The suggested approach is resistant to common image processing attacks, including rotation, cropping, and adding Gaussian noise, according to the findings of the experiments. The encryption of watermarked images in the spatial and DCT domains and fused watermarked images in the DCT domain are all discussed. The transparency and security of the method are assessed using various measurements. The proposed approach achieves high-quality reconstructed watermarks and high security by using encryption to images and achieves robustness against any obstructive attacks. The developed hybrid algorithm recovers the watermark even in the presence of an attack with a correlation near 0.8.
Hybrid Sampling and Similarity Attention Layer in Bidirectional Long Short Term Memory in Credit Card Fraud Detection

Machine learning methods are widely applied in credit card fraud detection to improve its efficiency and automate the process.The existing methods in credit card fraud detection have limitations of imbalance data problem.In this research, Hybrid Sampling (HS) -Similarity Attention Layer (SAL) -Bidirectional Long Short Term Memory (BiLSTM) is proposed to improve the classification performance of credit card fraud.Two datasets such as European data and Revolutionary analytics are applied to evaluate the SAL-BiLSTM model.Hybrid sampling of SMOTE-ENN model over-sample the minority class and under-sample majority class that helps to reduce difference between original data and generated data.The SAL is introduced to measure similarity of sequence of data to provide weight to unique features and reduces overfitting problem in classification.The proposed method has the advantage of removing the overlapped samples in the majority class and increasing the instances in the minority class.The SAL method in the proposed method helps to focus on unique features in the datasets to improve the classification performance.The BiLSTM model performs forward and backward analysis to find the relevant features for classification.The proposed HS-SAL-BiLSTM has a 99.2 % recall value and the existing RF-SMOTE-Support Vector Machine (SVM) has a 97.7 % recall value.
Breast Cancer Classification Using Customized ResNet Based Convolution Neural Networks

Deep learning is the most frequently used tool in the classification of tumors in medical applications.In recent decades, many research works have been done on the Breast Imaging Reporting & Data System (BI-RADS) atlas based classification of Breast cancer.As reported in the existing research works, training the larger datasets is a challenging task.Therefore, a customized ResNet based Convolution Neural Network (cRN-CNN) with batch normalization is proposed in this manuscript for addressing the above mentioned issue.The proposed cRN-CNN method has the advantage of faster training and computationally effective for the classification of BIRADS atlas based MRI breast cancer records, where the proposed model's performance is superior compared to the conventional CNN model.The extensive experiments performed on the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) dataset confirmed that the proposed cRN-CNN method achieved better classification results than the existing methods.In the proposed model, the deformation technique based on elastic deformation is also applied to increase the training size of data that helps to improve the outcomes of prediction up-to 99.80%, because of the efficient strategy of batch normalization as customization and elastic deformation.
Kelayakan Media Booklet Sawi Dayak (Elephantopus mollis Kunth) pada Pembelajaran Biologi Mahasiswa

Learning media can be arranged based on the results of literature studies from research on an object. This study aims to determine the feasibility of learning media booklet sawi dayak (Elephantopus mollis Kunth). The stages in this research include the stage of collecting information through a literature study on the efficacy of the mustard mustard plant, the booklet preparation stage, the booklet validation stage, and the booklet revision stage in accordance with the suggestions from the validators. Aspects that are assessed in the feasibility test are aspects of format, content, and language. The results of the validation were then analyzed using Gregory's calculations. The results of data analysis show that the aspects of format, content, and language each get a value of 1.00 and the average of these three aspects is 1.00 with a very high validity category. It is concluded that this booklet media can be used as a learning medium
PERM UNIVERSITY SCHOOL OF SCIENTIFIC PHILOSOPHY

This article provides a general characteristic and a coherent description of the Perm philosophical school. The activity of "Perm school of scientific philosophy" is considered stage by stage in its development. The relationship and place of the school in the system of Soviet Marxism are described: its peculiarities, tasks, and most prominent representatives. The unique conceptual language of the Perm philosophical school is presented. The work of the school is described on the basis of the non-colonial research approach. An attempt is made to reveal the unique features of Perm philosophical school and to fix its place on the philosophical map of the modern Russian thought. The aim of the article is to demonstrate a self-valuable experience of describing a regional philosophical school, without reducing this experience to general circumstances or unified evaluation scales. The method of personal or biographical description is chosen to reconstruct the activities and history of the school. The formation of the school and its main stages are presented through the views of its leader.
Improving Steering and Entanglement by Local Filtering Operation under Noisy Channels

In reality, a quantum system will inevitably interact with the environment around it, and this will lead to decoherence. In this paper, we mainly study quantum entanglement and Einstein–Podolsky–Rosen (EPR) steering of two-qubit system in some typical environments. Exactly, consider the initial two-qubit state to be the Werner-type state, we let one of the qubits pass through some different channels including amplitude damping (AD) channel, phase damping (PD) channel, phase flip (PF) channel, and bit flip (BF) channel. We research entanglement and EPR steering in the progress. Most importantly, we find that entanglement and EPR steering can be improved by local filtering operations with an appropriate k. Besides, we also give an example of the fact that the EPR steering is the proper subset of entanglement. These results will promote the application of the two quantum resources in quantum information tasks.
Using Technology to Compile an English-Arabic Glossary of the most Frequent Collocations in Ted Talks Parallel Corpus

Translation has evolved over the years and is now benefiting from the progress made in the field of technology and corpus linguistics. This study aims to use technology, particularly AntConc toolkit, to compile an English-Arabic glossary of the most frequent collocations listed in TED Talks parallel corpus. The glossary contains 1,000 unique English headwords and a total of 3,670 English collocations along with their Arabic translations as produced by Ted Talks translators and the researchers. Headwords in the glossary are alphabetically ordered and each collocation is accompanied with its frequency and its Arabic translation. The paper demonstrates the process through which the bilingual English-Arabic glossary has been compiled and the steps taken to process the collected data.
Supporting the Creation of Audio-Visual Content While Promoting Environmental Awareness: The 2G4N Project

Nowadays, social media platforms such as Facebook, Tik Tok and YouTube are the main tools for viewing and sharing audio-visual content. In this context, creating opportunities for students to create digital artifacts able to transmit sustainability and environmental concerns to a wider audience may harness the potential of the online participatory culture and make students active participants in the construction of their own knowledge. In this line of thought, the 2G4N project aims to understand to what extent approaches that combine the production of digital artifacts (namely short-videos) to communicate the importance of preserving the environment with in-nature activities, can foster in younger audiences the development of environmental awareness. To achieve this goal, the project is developing a Toolkit for Video Production, a physical artifact designed to support users with no specific skills in video planning and production to create videos, by using only their smartphone, to alert for environmental problems. The toolkit is being developed following a user-centred design approach, with the target audience involved in the toolkit’s design and validation process. It is expected that, by challenging young students to develop audio-visual contents and to share them in social media channels, this project’s approach may give younger audiences the opportunity to develop environmental awareness and also to competences related to storytelling, communication and creativity.
Penggunaan Set top box Bekas untuk Dimanfaatkan sebagai Cloud Server

The utilization of data storage media is currently very necessary to support the progress process in the digital era as it is today. One of the hardware needed to build personal data storage is HDD. But there are times when users need extra storage space using the internet or using the cloud. However, not all users can afford to rent or build an adequate server for securing personal data storage. Not all users need a server infrastructure that has high specifications to meet their needs. set top box is a device in the single-board computer category that is widely circulated in Indonesia. Set top box devices can be obtained at an affordable price, but can be a server. This research develops data storage based on a samba server application on a set top box device. Testing was done to change the default set top box operating system to the Linux operating system, the results showed that used set top box devices can be used as mini servers, the results of all the tests carried out obtained a result, namely a comparison of RAM and storage usage between the Pulpstone operating system and the system. armbian Linux operation has an increase in total RAM usage. Armbian Linux is greater than Pulpstone RAM usage which has an increase of 7.4% of the total RAM usage of the Pulpstone operating system, and data storage of the armbian Linux operating system has an increase of 74.4% from the data storage of the Pulsptone operating system, so Operating system changes can affect the total usage of RAM and data storage on the operating system used on the set top box device.
Nuclei spotting for computational pathology in microscopic images

Identification and classification of nuclei from microscopy is vital to new pharmaceutical developments. Biologist lacks a robust and efficient way to detect nuclei to natural variation in their appearances as well as differences in image capturing methods. Identification and classification of nuclei from microscopy images is considered as a complex task. A successful implementation will aid researchers immensely in their fight to find pharmaceutical solutions to medical crises while saving both valuable research time and funding. In this study, we employed a modified U-Net a deep learning based approach for nuclei detection where we computed 0.78 value of IOU (intersection over union) on BBBC038v1 dataset.&#x0D;
Exploring Countermeasures for the Problem of Music Score Recognition in Piano Teaching for the Visually Impaired

Students with visual impairment encounter many difficulties in the learning process of piano, the most prominent of which is the problem of reading music score.Due to objective constraints, visually impaired students have difficulty in quickly accessing information in music scores, which makes them slow, inefficient and prone to errors.At this point in time, piano teaching should change the traditional "score reading -sight-reading" training method, give full play to the strengths of the other senses of visually impaired students, reduce the impact of visual impairment, develop a suitable way of reading music for visually impaired students.In this paper, we study the common problem of reading music for visually impaired piano students, analyze the special situation of visually impaired piano students, sort out the causes and solutions of the problem of reading music in visually impaired piano teaching, and discuss the strategies to solve the problem of reading music in visually impaired piano teaching on the basis of actual teaching research.It is hoped that this study will provide a reference for piano educators who face such problems, and help visually impaired piano students to get rid of their difficulties in reading music, so that they can learn piano successfully.
Waste Management Practices of Small Hotels in Dehradun

Waste has emerged as a major issue in the modern society. Due to urbanization, the volume of waste is speedily rising. If the origin of waste creation makes no effort, then all wastes are either disposed of or burnt which results in waste of land, air, water, and energy as well as the growth of undesirable animals, plants and microorganism. Through the associate and involvement of the local population, waste may be managed locally in a sustainable manner. Waste management may be an expensive endeavor if not carried out appropriately. In most of the developing nations, 20-50 percent of urban expenditure budget is directed at management of solid waste. Small hotels in Dehradun, the capital city of Uttrakhand, haven't made any attempts to address their waste-related environmental consequences, which has had a negative effect on the environment. In this paper, waste management practices of small hotels in capital city of Uttrakhand are investigated. Employees of small hotels were given a survey schedule to fill out in order to collect information about their waste management practices. The findings show that small hotels choose to dispose of their waste rather than recycle and reuse it.
Realization of Deep Integration of Information Technology and University Education Development

Realization of deep integration of information technology and university education development is the key focus of this paper. We should then consider establishing a specification for the construction of digital resources for continuing education and a certification system for online education courses promote the establishment of a co-construction and sharing mechanism for high-quality digital education resources, and provide high-quality digital education resources for all types of learners in the whole society. This paper gives the novel ideas to consider the deep integration of information technology and university education. The details are discussed and application scenarios are considered as well.