Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Machine Learning Classifiers for Credit Card Fraud Detection

Now a day’s credit card transactions have been gaining popularity with the growth of e-commerce and shows tremendous opportunity for the future. Therefore, due to surge of credit card transaction, it is a crying need to secure it . Though the vendors and credit card providing authorities are showing dedication to secure the details of these transactions, researchers are searching new scopes or techniques to ensure absolute security which is the demand of time. To detect credit card fraud, along with other technologies,applications of machine learning and computational intelligence can be used and plays a vital role. For detecting credit card anomaly, this paper analyzes and compares some popular classifier algorithms. Moreover, this paper focuses on the performance of the classifiers. UCSD -FICO Data Mining Contest 2009 dataset were used to measure the performance of the classifiers. The final results of the experiment suggest that (1) meta and tree classifiers perform better than other types of classifiers, (2) though classification accuracy rate is high but fraud detection success rate is low. Finally, fraud detection rate  should be taken into consideration to assess the performance of the classifiers in a credit card fraud detection system.
Upright and grand piano actions dynamic performances assessments using a multibody approach

The behavior of the highly dynamic mechanical transmission between the key and the strings of pianos remains insufficiently understood. Called action, this mechanism is essential for the instrument playability and touch. Upright and grand piano actions, although based on similar principles, present quite different behaviors. This work outlines two models, one for each action, that have been carried out using a similar multibody approach with equivalent modeling hypotheses. The models take all the moving bodies into account as well as the intermittent contacts geometry and specific force laws. In addition, experimental validation with high-speed camera have been successfully achieved. Simulations of the models allow, among others, to estimate the maximal playing frequency, to discover the bridle strap and butt spring usefulness in the upright piano, to illustrate the fast repetition capability at a halfway keystroke in the grand piano action and to virtually adjust its settings. These results help in understanding the actions functioning and capabilities, and should contribute to a useful tool for piano makers, showing the interest of the multibody modeling approach for demystifying piano actions behavior and performances.
English language-learning environments in COVID-19 era: EFL contexts, English-language environments, technology-based approach, English language learning

No doubt that English education is a complex process, and its successes depend on many variables. Of these, English language-learning environments for regular practice are commonly accepted as a prerequisite in the contexts of English as a foreign language (EFL). Unfortunately, the questions of what English language-learning environments exactly are and how these are to be implemented have not been well addressed. The present paper reviews the literature on the concept of English language-learning environments and identifies approaches for successful applications of those environments for English language education. The present paper contributes to the growing body of literature on foreign language education and draws special attention to English language teaching theories (ELT).
Attack Graph Generation

This chapter presents a review of the state of the art on attack graph generation methods and their respective models, a review of possible sources of vulnerability, weakness, and remediation information, a comparative analysis of 15 tools for attack graph generation, and a case study on the implementation of a production-ready system and its challenges. This review mostly covers pre/post-condition attack graph models, as the majority of works in the literature are based on them, and discusses the information needs of each. Fifteen semi-structured vulnerability databases (e.g., the National Vulnerability Database) will be presented in a comparative analysis focused on the richness of their information and their ability to fulfill each model's requirements. Furthermore, the Common Weakness Enumeration list will be briefly presented, as its use can further enrich the expressiveness of the pre/post-condition models. As the main purpose of attack graphs is the calculation of effective remediation actions, a review of possible remediation information sources will be conducted and the possible challenges posed by the information extraction process will be discussed. Finally, a number of attack graph generation tools will be presented from the perspective of their graph building algorithms and information requirements, in conjunction with a case study on the practical challenges faced when implementing a production-ready system.
The Drivers and Virality of Hate Speech Online

Hateful speech online is a prevalent challenge for digital platforms. As hate speech and online bullying are particular issues among young children, this paper documents the extent of hateful speech in response to the videos posted by young children who are attempting to attract large followings on the digital content sharing site YouTube.
The Case for Establishing a Blockchain Research and Development Program at an Academic Medical Center

Objective: To develop a research and development program to study factors that will support research, education and innovation using blockchain technology for health in an effective and sustainable manner. We proposed to conduct qualitative research to generate insights for developing a market strategy to build a research lab for the promotion of blockchain technologies in health in academic environments. The team aimed to identify the key barriers and opportunities for developing a sustainable research lab that generates research, education, and application of blockchain in healthcare at an academic medical institution and test those strategies in a real-world scenario. Methods: The research team identified potential customers and stakeholders through interviews and snowball sampling. The team conducted semi-structured interviews with 4 faculty researchers, 10 industry leaders, and 6 students from a variety of disciplines and organizations. The findings of these research activities informed our understanding of the needs of stratified customers and helped identify key assets and activities the lab will have to offer to meet those needs. Results: The research insights from data analysis were used to build the business model for establishing a blockchain in health impact lab. This systematic study of areas where blockchain technology can impact health will guide the future development of research agenda for the researchers on campus. Conclusion: Based on our learnings, we hope to design a Blockchain in Health Impact Lab to serve as a platform for students and faculty to come together with industry partners and explore current challenges of blockchain in healthcare. The academic medical center’s partnership with other healthcare providers will help create real-world opportunities to demonstrate and implement new technologies.
An Improved Dual Steganography Model Using Multi-pass Encryption and Quotient Value Differencing

The proposed work suggests a high capacity steganography technique using dual steganography approach.It utilizes a Multi pass encryption system to scramble the mystery message before hiding.The scrambled mystery message is divided into two parts and concealed utilizing two duplicates of the same cover image.One copy of the image uses a modified Quotient value differencing approach to hide 1st part of secret message and another copy uses difference expansion method to embed the 2nd part of secret messages.It provides more secrecy because the suggested work uses the benefits of both encryption and steganography.The investigation shows that the two stego-pictures provide high embedding capability (EC) of 1848153 bits with Peak signal to noise ratio PSNR1 of 47.2 dB and PSNR2 of 51.1 dB.Further, examination with other existing methodologies shows that the suggested methodology is proven to be better in Structural similarity Index (SSIM), EC and PSNR.
Interactive Proofs for Verifying Machine Learning.

We consider the following question: using a source of labeled data and interaction with an untrusted prover, what is the complexity of verifying that a given hypothesis is approximately correct? We study interactive proof systems for PAC verification, where a verifier that interacts with a prover is required to accept good hypotheses, and reject bad hypotheses. Both the verifier and the prover are efficient and have access to labeled data samples from an unknown distribution. We are interested in cases where the verifier can use significantly less data than is required for (agnostic) PAC learning, or use a substantially cheaper data source (e.g., using only random samples for verification, even though learning requires membership queries). We believe that today, when data and data-driven algorithms are quickly gaining prominence, the question of verifying purported outcomes of data analyses is very well-motivated.We show three main results. First, we prove that for a specific hypothesis class, verification is significantly cheaper than learning in terms of sample complexity, even if the verifier engages with the prover only in a single-round (NP-like) protocol. Moreover, for this class we prove that single-round verification is also significantly cheaper than testing closeness to the class. Second, for the broad class of Fourier-sparse boolean functions, we show a multi-round (IP-like) verification protocol, where the prover uses membership queries, and the verifier is able to assess the result while only using random samples. Third, we show that verification is not always more efficient. Namely, we show a class of functions where verification requires as many samples as learning does, up to a logarithmic factor.
Perception of the Quality of Educational System for Entrepreneurship – Comparative Analysis

High-quality entrepreneurial education has a positive influence on SMEs' development, the establishment of start-ups, business innovation, and many macroeconomic indicators.The aim of this study is to examine and evaluate the quality of the educational system from the standpoint of SME owners and managers.The analysis is aimed at detecting connections between respective respondent categories based on their region, type of business, industry, length of business operation, attained employees´ education, and gender.The comparative analysis aims at examining the differences between Czech and Slovak SME entrepreneurs' perceptions of the educational system quality.The research results prove that selected differential viewpoints enable us to see the differences in how the quality of high school and university education is perceived, partially due to managers' diverse demands as to the workforce in their respective enterprises, its integration into the work process, and the job position.This micro-view is complemented by a macro-viewdemands and requirements regarding the workforce are differentiated also by the size of the enterprise, its type, length of operation, etc. in both countries.The results provide valuable information for the authors of economic and educational policies, as well as for regional strategic development planning.They also emphasize the importance of a systematic approach in solving the issue of entrepreneurial education and the need to ensure support for enterprises on all levels of education, including lifelong learning.
Self-Weighted Supervised Discriminative Feature Selection via Redundancy Minimization

Feature selection plays a key role in many machine learning problems.Especially as an important data preprocessing method, robust and pragmatic feature selection methods can be applied to extract meaningful features and eliminate redundant ones.As we all known, many feature selection methods select features by using some certain feature evaluation criteria to obtain the corresponding score for every feature, such that we can select high score features.Unfortunately, correlated features usually connect with each other, which may result in large correlations between top ranked features, such that the redundancy among the selected features is brought about.To solve this problem, we introduce the redundancy matrix A in the AGRM (a novel auto-weighted feature selection framework via global redundancy minimization) framework.Meanwhile, we introduce the adaptive redundancy matrix S and treat the redundancy matrix S as an optimizing variable, rather than setting the redundant matrix S as a prior.In addition, we propose a robust algorithm to efficiently address the constrained optimization problem.Finally, extensive experiments on six datasets show the superiority of our proposed method.
Minipatch Learning as Implicit Ridge-Like Regularization

Ridge-like regularization often leads to improved generalization performance of machine learning models by mitigating overfitting. While ridge-regularized machine learning methods are widely used in many important applications, direct training via optimization could become challenging in huge data scenarios with millions of examples and features. We tackle such challenges by proposing a general approach that achieves ridge-like regularization through implicit techniques named Minipatch Ridge (MPRidge). Our approach is based on taking an ensemble of coefficients of unregularized learners trained on many tiny, random subsamples of both the examples and features of the training data, which we call minipatches. We empirically demonstrate that MPRidge induces an implicit ridge-like regularizing effect and performs nearly the same as explicit ridge regularization for a general class of predictors including logistic regression, SVM, and robust regression. Embarrassingly parallelizable, MPRidge provides a computationally appealing alternative to inducing ridge-like regularization for improving generalization performance in challenging big-data settings.
Automated machine learning can classify bound entangled states with tomograms

For quantum systems with a total dimension greater than six, the positive partial transposition (PPT) criterion is sufficient but not necessary to decide the non-separability of quantum states. Here, we present an Automated Machine Learning approach to classify random states of two qutrits as separable or entangled using enough data to perform a quantum state tomography, without any direct measurement of its entanglement. We could successfully apply our framework even when the Peres-Horodecki criterion fails. In addition, we could also estimate the Generalized Robustness of Entanglement with regression techniques and use it to validate our classifiers.
Efficient 3D Neural Networks with Support Vector Machine for Hippocampus Segmentation

Accurate segmentation of the hippocampal and its subfields from the brain magnetic resonance imaging (MRI), which is a prerequisite for volume measurement, plays a significant role in the clinical diagnosis and treatment of many neurodegenerative diseases. It is of great significance for the precise segmentation of the hippocampus and its sub-regions.In this paper, we proposed a hippocampal subfields segmentation approach based on support vector machine (SVM) combined 3D convolutional neural network (3D CNN) and generative adversarial network (GAN). In the 3D CNN-SVM model, the representative features processed by the 3D CNN are input into the SVM. SVM is trained with the features to achieve the voxel classification of the image, and the segmentation results are obtained. In the 3D GAN-SVM model, we use the generator to segment and use the 3D CNN-SVM network we proposed as the discriminator.The experiments has performed on the dataset obtained from Center for Imaging of Neurodegenerative Diseases (CIND) in San Francisco, USA. The segmentation dice similarity coefficients (DSCs) of the 3D CNN-SVM for CAI, CA2, DG, CA3, Head, Tail, SUB, ERC and PHG in hippocampal subfields are respectively 0.930, 0.926, 0.977, 0.967, 0.931, 0.905, 0.981, 0.870 and 0.911. It demonstrates that combining 3D CNN and SVM achieves a significant improvement in the accuracy of all the hippocampal subfields, and outperforms the existing methods based on the CNN. The DSCs of 3D GAN-SVM are higher, which are respectively 0.989, 0.965, 0.986, 0.977, 0.975, 0.993, 0.818, 0.985 and 0.994. The effect of the GAN-SVM model is also significantly better than that of pure GAN, and the segmentation accuracy has reached the best level on this dataset.Neural network can extract representative features, but it mainly relies on extracting features from a large number of accurately labeled datasets. Most medical datasets are small and difficult to obtain. SVM is more suitable for classification of small datasets, so we combine SVM and neural network to effectively improve the segmentation accuracy of hippocampus in brain MRI images.
Insights on Mineralogy and Chemistry of Fairview Gold Mine, Barberton Greenstone Belt, South Africa

DOI: 10.17014/ijog.8.1.73-99 Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and electron microprobe analysis (EMPA) techniques were used to determine the mineralogy and chemistry of dominant sulphide and associated gold deposits at the Fairview Gold Mine of the Barberton Greenstone Belt (BGB). The rocks and ore mineral associations at the Fairview Mine indicate mesothermal (orogenic) conditions for the gold deposit. The whole rock chemistry of the Fairview Mine reflects calc-alkaline affinities, and felsic provenance is revealed by high SiO2, K2O, Al2O3, and TiO2, and low Fe2O3, MgO, Ni, and Cr contents. The mineral assemblages of the Fairview Mine rocks and ores indicate three phases of metamorphisms; regional prograde, thermal, and retrograde metamorphism, which caused zonation and heterogeneity of the ore minerals. The principal ore minerals in this deposit are pyrite, arsenopyrite, pyrrhotite, chalcopyrite, and rare sphalerite, as well as minor gold. The obtained results revealed that sulphides of the Fairview Mine are divided into two types (generations) varying in their morphology, association, and elemental composition. The old type 1 is porous, anhedral, heterogeneous, contains inclusions and rich in As, Ni, Co, and Au compared to type 2. Trace-element distribution and occurrence mode of gold at Fairview mine reflect two phases of gold mineralization. The first stage is associated with sulphides (mainly pyrite and arsenopyrite), whilst the second phase is free-lode hosted by silicates (mainly quartz).
COMPARATIVE ANALYSIS OF SOFTWARE EFFORT ESTIMATION USING DATA MINING TECHNIQUE AND FEATURE SELECTION

Software development involves several interrelated factors that influence development efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time and budget control in software development. Software Effort Estimation or software cost/effort estimation can help a software development company to overcome difficulties experienced in estimating software development efforts. This study aims to compare the Machine Learning method of Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree Random Forest (DTRF) to calculate estimated cost/effort software. Then these five approaches will be tested on a dataset of software development projects as many as 10 dataset projects. So that it can produce new knowledge about what machine learning and non-machine learning methods are the most accurate for estimating software business. As well as knowing between the selection between using Particle Swarm Optimization (PSO) for attributes selection and without PSO, which one can increase the accuracy for software business estimation. The data mining algorithm used to calculate the most optimal software effort estimate is the Linear Regression algorithm with an average RMSE value of 1603,024 for the 10 datasets tested. Then using the PSO feature selection can increase the accuracy or reduce the RMSE average value to 1552,999. The result indicates that, compared with the original regression linear model, the accuracy or error rate of software effort estimation has increased by 3.12% by applying PSO feature selection
A Multi-Disciplinary Perspective for Conducting Artificial Intelligence-enabled Privacy Analytics

Events such as Facebook-Cambridge Analytica scandal and data aggregation efforts by technology providers have illustrated how fragile modern society is to privacy violations. Internationally recognized entities such as the National Science Foundation (NSF) have indicated that Artificial Intelligence (AI)-enabled models, artifacts, and systems can efficiently and effectively sift through large quantities of data from legal documents, social media, Dark Web sites, and other sources to curb privacy violations. Yet considerable efforts are still required for understanding prevailing data sources, systematically developing AI-enabled privacy analytics to tackle emerging challenges, and deploying systems to address critical privacy needs. To this end, we provide an overview of prevailing data sources that can support AI-enabled privacy analytics; a multi-disciplinary research framework that connects data, algorithms, and systems to tackle emerging AI-enabled privacy analytics challenges such as entity resolution, privacy assistance systems, privacy risk modeling, and more; a summary of selected funding sources to support high-impact privacy analytics research; and an overview of prevailing conference and journal venues that can be leveraged to share and archive privacy analytics research. We conclude this paper with an introduction of the papers included in this special issue.
Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters

We consider the problem of estimating an undirected Gaussian graphical model when the underlying distribution is multivariate totally positive of order 2 (MTP2), a strong form of positive dependence. Such distributions are relevant for example for portfolio selection, since assets are usually positively dependent. A large body of methods have been proposed for learning undirected graphical models without the MTP2 constraint. A major limitation of these methods is that their structure recovery guarantees in the high-dimensional setting usually require a particular choice of a tuning parameter, which is unknown a priori in real world applications. We here propose a new method to estimate the underlying undirected graphical model under MTP2 and show that it is provably consistent in structure recovery without adjusting the tuning parameters. This is achieved by a constraint-based estimator that infers the structure of the underlying graphical model by testing the signs of the empirical partial correlation coefficients. We evaluate the performance of our estimator in simulations and on financial data.