Reviewerly/exHarmony huggingface dataset:
An Effective Data Sampling Procedure for Imbalanced Data Learning on Health Insurance Fraud Detection

Fraud detection has received considerable attention from many academic research and industries worldwide due to its increasing popularity. Insurance datasets are enormous, with skewed distributions and high dimensionality. Skewed class distribution and its volume are considered significant problems while analyzing insurance datasets, as these issues increase the misclassification rates. Although sampling approaches, such as random oversampling and SMOTE can help balance the data, they can also increase the computational complexity and lead to a deterioration of model's performance. So, more sophisticated techniques are needed to balance the skewed classes efficiently. This research focuses on optimizing the learner for fraud detection by applying a Fused Resampling and Cleaning Ensemble (FusedRCE) for effective sampling in health insurance fraud detection. We hypothesized that meticulous oversampling followed with a guided data cleaning would improve the prediction performance and learner's understanding of the minority fraudulent classes compared to other sampling techniques. The proposed model works in three steps. As a first step, PCA is applied to extract the necessary features and reduce the dimensions in the data. In the second step, a hybrid combination of k-means clustering and SMOTE oversampling is used to resample the imbalanced data. Oversampling introduces lots of noise in the data. A thorough cleaning is performed on the balanced data to remove the noisy samples generated during oversampling using the Tomek Link algorithm in the third step. Tomek Link algorithm clears the boundary between minority and majority class samples and makes the data more precise and freer from noise. The resultant dataset is used by four different classification algorithms: Logistic Regression, Decision Tree Classifier, k-Nearest Neighbors, and Neural Networks using repeated 5-fold cross-validation. Compared to other classifiers, Neural Networks with FusedRCE had the highest average prediction rate of 98.9%. The results were also measured using parameters such as F1 score, Precision, Recall and AUC values. The results obtained show that the proposed method performed significantly better than any other fraud detection approach in health insurance by predicting more fraudulent data with greater accuracy and a 3x increase in speed during training.
Designing and Implementing a Database for Thesis Data Management by Using the Python Flask Framework

This study aims to implement the database design used in thesis data processing using Flask. The results of the database design produce four tables, namely the lecturer table is used to store lecturer data, the student table is used to store student data, the title of thesis table is used to store data related to student thesis and the guidance table contains history during guidance with the supervisor, The design of the thesis data processing database was developed using Flask. Flask is a web framework written in Python. Flask serves as the application framework and the interface of the web-based flask application. In this research, the methodology used is to implement database development which consists of system analysis, logical design, physical design and implementation. The database design methodology is carried out by 1. Building a local conceptual data design based on user views, 2. Building and validating local logical data models, 3. Building and validating logical data models, 4. Translating logical data models for the database management system, 5. Designing and implementation of physical representation. The results show that the process of creating a thesis data management database can be done using the Flask framework. The development process using the flask framework includes the process of creating databases, tables and reading, adding, repairing and deleting data. In making the application, the application is divided into 2, namely the application used for database management and the application used to display website-based results that are used to display the results of data processing from the application. Implementation of data structures in the database design and applications used include programming language: Python 3.7.0, framework for displaying web-based data: Flask 1.1.2, and database management system using SQLite.
Modeling image aesthetics through aesthetics-related attributes

Automatic assessment of image aesthetics is a challenging task for the computer vision community that has a wide range of applications.The most promising state-of-the-art approaches are based on deep learning methods that jointly predict aesthetics-related attributes and aesthetics score.In this article, we propose a method that learns the aesthetics score on the basis of the prediction of aesthetics-related attributes.To this end, we extract a multi-level spatially pooled (MLSP) features set from a pretrained ImageNet network and then these features are used to train a Multi Layer Perceptron (MLP) to predict image aesthetics-related attributes.A Support Vector Regression machine (SVR) is finally used to estimate the image aesthetics score starting from the aesthetics-related attributes.Experimental results on the "Aesthetics with Attributes Database" (AADB) demonstrate the effectiveness of our approach that outperforms the state of the art of about 5.5% in terms of Spearman's Rankorder Correlation Coefficient (SROCC).
The HayWired Earthquake Scenario—Societal Consequences

The HayWired earthquake scenario, led by the U.S. Geological Survey (USGS), anticipates the impacts of a hypothetical moment magnitude 7.0 earthquake on the Hayward Fault.The fault runs along the east side of California's San Francisco Bay and is among the most active and dangerous in the United States, passing through a densely urbanized and interconnected region.A scientifically realistic scenario is one way to learn from a large earthquake before one occurs in the bay region.The USGS and its partners in the HayWired Coalition are working to energize residents and businesses to engage in new and ongoing efforts to prepare the region for such a future earthquake. 234,202Population affected at home only 181,856 Population affected at work place only 96,292 Population affected at home and work 2.6 million Population little affected at home and work Concentrated building damage from earthquake and fire hazards found in the yellow and orange tracts in the above map.Numbers are origin-destination data from the Longitudinal Employer-Household Dynamics database (2015 version) U.S.
axiological approach to the training of students of pedagogical universities

The axiological approach to the education impacts future professional musician’s values and ability to solve professional problems and challenges. This research is aimed at studying the principles of the axiological approach and its possible application in the pedagogical process. Theoretical and practical research on the use of axiological approach in the theory and practice of music pedagogical education was conducted. Pedagogical methods such as questionnaire, observation, testing, group and individual interviews were used. The definition of the axiological approach was given. Effective means of its developing were presented. It was found that integration of axiological, systemic, culturological approaches allows to deepen and diversify the structural and semantic characteristics of the axiology sphere of higher institutions of music and music-pedagogical education. The prospects for further research were found including the necessity to identify the specifics of the development of axiological competence in students of higher art educational institutions.
Art education development in the context of global changes

New trends are emerging in the system of higher art education due to globalisation and integration, rapid development of technological innovations. The use of technological innovations causes emerging of new types of communication, collaboration and interaction between teachers and students. In the article, the concept of smart education and its principles and the competence of a future teacher and specifics of their professional training were described. Global educational trends were characterised. The perspectives of professional and pedagogical activity of art teacher were outlined. The importance of technological innovations in the process of innovative education was explained. The criteria for assessing the teacher's innovative competence were highlighted. It was established that the modern smart society requires a teacher who teaches art history while constantly developing his cognitive abilities, systematically renew his intellectual and creative potential. The peculiarities of teacher's innovative competence formation in the changing conditions of a modern “smart” society were investigated. It was concluded that digital competence is one of the new requirements for art critics training in the context of the development of the information-oriented society.
An Effective Text Classifier using Machine Learning for Identifying Tweets’ Polarity Concerning Terrorist Connotation

Terrorist groups in the Arab world are using social networking sites like Twitter and Facebook to rapidly spread terror for the past few years. Detection and suspension of such accounts is a way to control the menace to some extent. This research is aimed at building an effective text classifier, using machine learning to identify the polarity of the tweets automatically. Five classifiers were chosen, which are AdB_SAMME, AdB_SAMME.R, Linear SVM, NB, and LR. These classifiers were applied on three features namely S1 (one word, unigram), S2 (word pair, bigram), and S3 (word triplet, trigram). All five classifiers evaluated samples S1, S2, and S3 in 346 preprocessed tweets. Feature extraction process utilized one of the most widely applied weighing schemes tf-idf (term frequency-inverse document frequency).The results were validated by four experts in Arabic language (three teachers and an educational supervisor in Saudi Arabia) through a questionnaire. The study found that the Linear SVM classifier yielded the best results of 99.7 % classification accuracy on S3 among all the other classifiers used. When both classification accuracy and time were considered, the NB classifier demonstrated the performance on S1 with 99.4% accuracy, which was comparable with Linear SVM. The Arab world has faced massive terrorist attacks in the past, and therefore, the research is highly significant and relevant due to its specific focus on detecting terrorism messages in Arabic. The state-of-the-art methods developed so far for tweets classification are mostly focused on analyzing English text, and hence, there was a dire need for devising machine learning algorithms for detecting Arabic terrorism messages. The innovative aspect of the model presented in the current study is that the five best classifiers were selected and applied on three language models S1, S2, and S3. The comparative analysis based on classification accuracy and time constraints proposed the best classifiers for sentiment analysis in the Arabic language.
Mobile-Assisted Language Learning (MALL) in English language acquisition: a critical literature review

The objective of this study is ‘how effective mobile apps in learning English?’ by exploring the use of mobile apps in English as a Second Language (ESL)/English as a Foreign Language (EFL) acquisition. It examines how employing MALL in learning English can improve students’ receptive skills, productive skills, and other skills by conducting critical literature review to answer the research question. The results showed that employing mobile applications in learning English is effective to develop L2 learners’ English skills and also Mobile apps help L2 learners in learning English both as the second and foreign language. Interestingly, in the situation of the global pandemic which many schools are closed, mobile technology has helped students in learning English skills. It can be suggested that by employing Mobile Assisted Language Learning (MALL) in learning English skills plays a crucial role in the situation of the global pandemic of COVID 19 where teachers and students can use mobile apps both in and outside the classroom because it is convenient and user friendly.
study of awarding student achievement using blockchain

Blockchain technology will soon have progressed from being an innovation to the norm of application development and the digital revolution. The technology of a blockchain is essential based on a widely distributed, peer-to-peer ledger of transactions and balances. This ledger is essentially a huge database broken down into chunks known as blocks. These blocks and the entire ledger are stored in millions of copies on that particular blockchain network on extremely powerful computers known as nodes. There is a majority-based consensus that exists for every transaction that is carried out and put in a block. To tamper with this ledger would require computing power either equal to or greater than 51% of all the nodes active for that particular blockchain. Even if this power were acquired to tamper with or hack the particular blockchain, the cryptocurrencies or tokens that are transacted on the blockchain would essentially be overturned and considered worthless due to the other nodes detecting a hack or attempt to steal tokens or wallet balances. This is how the first cryptocurrency like bitcoin, along with many other cryptocurrencies on their respective blockchain technologies, operate.
TADC: A Topic-Aware Dynamic Convolutional Neural Network for Aspect Extraction

Aspect extraction is one of the key tasks in fine-grained sentiment analysis. This task aims to identify explicit opinion targets from user-generated documents. Currently, the mainstream methods for aspect extraction are built on recurrent neural networks (RNNs), which are difficult to parallelize. To accelerate the training/testing process, convolutional neural network (CNN)-based methods are introduced. However, such models usually utilize the same set of filters to convolve all input documents, and hence, the unique information inherent in each document may not be fully captured. To alleviate this issue, we propose a CNN-based model that employs a set of dynamic filters. Specifically, the proposed model extracts the aspects in a document using the filters generated from the aspect information intrinsic in the document. With the dynamically generated filters, our model is capable of learning more important features concerning aspects, thus promoting the effectiveness of aspect extraction. Furthermore, considering that aspects can be grouped into certain topics that conversely indicate the target words that need to be extracted, we naturally introduce a neural topic model (NTM) and integrate latent topics into the CNN-based module to help identify aspects. Experiments on two benchmark datasets demonstrate that the joint model is able to effectively identify aspects and produce interpretable topics.
A Fair and Privacy-Preserving Image Trading System Based on Blockchain and Group Signature

With the rise of digital images in our daily lives, there is a growing need to provide an image trading market where people can monetize their images and get desired images at prices that fit their budget. Those images are usually uploaded and stored onto centralized image trading service providers’ servers and the transactions for image trading are processed by these providers. Unfortunately, transaction unfairness and users’ privacy breaches have become major concerns since the service providers might be untrusted and able to manipulate image trading prices and infer users’ private information. Recently, several approaches have been proposed to address the unfairness issue by using the decentralized ledger technique and smart contract, but users’ privacy protection is not considered. In this paper, we propose a fair and privacy-preserving protocol that supports image fair exchange and protect user privacy. In particular, we exploit blockchain and Merkle tree to construct a fair image trading protocol with low communication overhead based on smart contract, which serves as an external judge that resolves disputes between buyers and sellers in image transactions. Moreover, we extend a popular short group signature scheme to protect users’ identity privacy, prevent linkability of transactions from being inferred, and ensure traceability of malicious users who may sell fake images and/or refuse to pay. Finally, we design and build a practical and open-source image trading system to evaluate the performance of our proposed protocol. Experimental results demonstrate its effectiveness and efficiency in real-world applications.
To App or Not to App? Understanding Public Resistance to COVID-19 Digital Contact Tracing and its Criminological Relevance

In the context of the COVID-19 pandemic, digital contact tracing has been developed and promoted in many countries as a valuable tool to help the fight against the virus, allowing health authorities to react quickly and limit contagion. Very often, however, these tracing apps have faced public resistance, making their use relatively sparse and ineffective. Our study relies on an interdisciplinary approach that brings together criminological and computational expertise to consider the key social dynamics underlying people’s resistance to using the NHS contact-tracing app in England and Wales. The present study analyses a large Twitter dataset to investigate interactions between relevant user accounts and identify the main narrative frames (lack of trust and negative liberties) and mechanisms (polluted information, conspiratorial thinking and reactance) to explain resistance towards use of the NHS contact-tracing app. Our study builds on concepts of User eXperience (UX) and algorithm aversion and demonstrates the relevance of these elements to the key criminological problem of resistance to official technologies.
The state of the art of the blockchain ethics in healthcare: A systematic literature review

Blockchain is a software innovation which is based on a cryptographically secured, decentralised, and distributed storage of data. The technological breakthrough was done as a part of and became familiar through cryptocurrencies, where it is used to openly store currency transactions among its users. Blockchain technology has been since proposed and used in various domains ranging from open contracts to electronic voting—as well as in various purposes in eHealth, medical and well-being applications. However, its usage in these sectors possesses several ethical questions as these environments are full of personal and private patient information. To study the state-of-the-art of the blockchain ethics in healthcare, this study presents a systematic literature study (SLR) on this phenomenon. By collecting the relevant primary studies from Scopus, the results show that the utilisation of blockchain is swiftly maturing with new research and applications published constantly in this domain. However, the ethical discussion related to the use of blockchain technologies is still taking its baby steps in healthcare. Despite a few openings, ethical research is practically non-existing when compared against the full extant literature on the topic. Therefore, remarkable amount of further work is needed to cover the potential ethical questions related to the adoption and use of the technology.
Relaxed Dissimilarity-based Symbolic Histogram Variants for Granular Graph Embedding

Graph embedding is an established and popular approach when designing graph-based pattern recognition systems.Amongst the several strategies, in the last ten years, Granular Computing emerged as a promising framework for structural pattern recognition.In the late 2000's, symbolic histograms have been proposed as the driving force in order to perform the graph embedding procedure by counting the number of times each granule of information appears in the graph to be embedded.Similarly to a bag-of-words representation of a text corpora, symbolic histograms have been originally conceived as integer-valued vectorial representation of the graphs.In this paper, we propose six 'relaxed' versions of symbolic histograms, where the proper dissimilarity values between the information granules and the constituent parts of the graph to be embedded are taken into account, information which is discarded in the original symbolic histogram formulation due to the hard-limited nature of the counting procedure.Experimental results on six open-access datasets of fully-labelled graphs show comparable performance in terms of classification accuracy with respect to the original symbolic histograms (average accuracy shift ranging from -7% to +2%), counterbalanced by a great improvement in terms of number of resulting information granules, hence number of features in the embedding space (up to 75% less features, on average).
Comparison of deep learning models in terms of multiple object detection on satellite images

The images obtained by remote sensing contain important data about ground surface. It is an important issue to detect objects on the ground surface with these images. Deep learning models are known to give better results in studies on object detection. However, the superiority of the deep learning models over each other is unknown. For this reason, it should be clarified which model is superior in terms of object detection and which model should be used in studies. In this study, it was aimed to reveal the superiorities of deep learning models by comparing their performance in detecting multiple objects. By using 11 deep learning models that are frequently encountered in the literature, the application of detecting objects of 14 classes in the DOTA dataset were made. 49,053 objects in 888 images were used for training by using AlexNet, Vgg16, Vgg19, GoogleNet, SequezeeNet, Resnet18, Resnet50, Resnet101, Inceptionresnetv2, inceptionv3, DenseNet201 models. After the training, 13,772 objects consisting of 14 classes in 277 images were used for testing with RCNN, which is one of the object detection methods. The performance of each algorithm in 14 classes has been demonstrated by using Average Precision (AP) and Mean Average Precision (mAP) to measure the performance of the models from their metrics. In a particular class of each deep learning model, difference in performance was observed The model with the highest performance varies in each class. In the application, the most successful average mAP value of 14 classes was Vgg16 with 24.64, while the lowest was InceptionResnetV2 with 11.78. In this article, the success of deep learning models in detecting multiple objects has been demonstrated practically and it is thought to be an important resource for researchers who will study on this subject.