Reviewerly/exHarmony huggingface dataset:
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

Due to the rapid growth of the E-Commerce industry, the use of credit cards for online purchases has increased dramatically. In recent years, credit card fraud is becoming a major complication for banks as it has become very difficult for detecting fraud in the credit card system. To overcome this hardship Machine learning plays an eminent role in detecting the credit card fraud in the transactions. Modeling prior credit card transactions with data from ones that turned out to be fraudulent is part of the Card Fraud Detection Problem. In Machine learning the machine is trained at first to predict the output so, to predict the various bank transactions various machine learning algorithms are used.  The SMOTE approach was employed to oversample the dataset because it was severely unbalanced. This paper  the examines and overview the performance of K-nearest neighbors, Decision Tree, Logistic regression and Random forest, XGBoost  for credit card fraud detection. The assignment is implemented in Python and uses five distinct machine learning classification techniques. The performance of the algorithm is evaluated by accuracy score, confusion matrix, f1-score, precision and recall score and auc-roc curve as well.
Polynomial and trigonometric splines

Classes of simple polynomial and simple trigonometric splines given by Fourier series are considered. It is shown that the class of simple trigonometric splines includes the class of simple polynomial splines. For some parameter values, the polynomial splines coincide with the trigonometric ones; this allows to transfer to such trigonometric splines all the results obtained for polynomial splines. Thus, it was possible to combine two powerful theories - the theory of trigonometric Fourier series and the theory of simple polynomial splines. The above material is illustrated by numerous examples.
Peer Conformity, Drug Abuse Intentions, and Gender Against Drug Abuse Behavior in Adolescents in Tebo Ilir District, Jambi

This study aims to determine the effect of conformity, intention, and gender toward drug abuse behavior in Tebo Ilir Jambi District. The subjects of this study were 360 adolescents aged 15-21 years old, who live in Tebo Ilir District, Tebo Regency, Jambi Province. The data analysis in this study used multiple linear regression with the dummy method of the SPPS 26.0 for windows application. The hypothesis proposed is conformity, intention, and the influence of gender on drug abuse behavior. The results of this study indicate that conformity, intention, and gender affect drug abuse behavior with an R Square value of 0.752 with a significance value of (p= 0,000 <0,05). In addition, conformity has significant positive role toward drug abuse behavior (p= 0,000< 0,05), intention has significant positive role toward drug abuse behavior (p= 0,000< 0,05) and gender has significant positive role toward drug abuse behavior (p= 0,000<0,05). The male was involved in drug abuse behavior higher than the female with a mean value of 24.27, while the female was 12.39. Drug abuse behavior in adolescents is included in the moderate category with 296 adolescents out of 360 adolescents. The next result stated that 64 teenagers were in the highest category. This means that from 360 research respondents tend to use drugs.
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis

As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a deeper understanding of these explanations is still lacking. In this work, we systematically analyze counterfactual explanations through the lens of adversarial examples. We do so by formalizing the similarities between popular counterfactual explanation and adversarial example generation methods identifying conditions when they are equivalent. We then derive the upper bounds on the distances between the solutions output by counterfactual explanation and adversarial example generation methods, which we validate on several real-world data sets. By establishing these theoretical and empirical similarities between counterfactual explanations and adversarial examples, our work raises fundamental questions about the design and development of existing counterfactual explanation algorithms.
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

Federated learning is a powerful distributed learning scheme that allows numerous edge devices to collaboratively train a model without sharing their data. However, training is resource-intensive for edge devices, and limited network bandwidth is often the main bottleneck. Prior work often overcomes the constraints by condensing the models or messages into compact formats, e.g., by gradient compression or distillation. In contrast, we propose ProgFed, the first progressive training framework for efficient and effective federated learning. It inherently reduces computation and two-way communication costs while maintaining the strong performance of the final models. We theoretically prove that ProgFed converges at the same asymptotic rate as standard training on full models. Extensive results on a broad range of architectures, including CNNs (VGG, ResNet, ConvNets) and U-nets, and diverse tasks from simple classification to medical image segmentation show that our highly effective training approach saves up to $20\%$ computation and up to $63\%$ communication costs for converged models. As our approach is also complimentary to prior work on compression, we can achieve a wide range of trade-offs by combining these techniques, showing reduced communication of up to $50\times$ at only $0.1\%$ loss in utility. Code is available at https://github.com/a514514772/ProgFed.
On the invertibility of a voice privacy system using embedding alignement

This paper explores various attack scenarios on a voice anonymization system using embeddings alignment techniques. We use Wasserstein-Procrustes (an algorithm initially designed for unsupervised translation) or Procrustes analysis to match two sets of x-vectors, before and after voice anonymization, to mimic this transformation as a rotation function. We compute the optimal rotation and compare the results of this approximation to the official Voice Privacy Challenge results. We show that a complex system like the baseline of the Voice Privacy Challenge can be approximated by a rotation, estimated using a limited set of x-vectors. This paper studies the space of solutions for voice anonymization within the specific scope of rotations. Rotations being reversible, the proposed method can recover up to 62% of the speaker identities from anonymized embeddings.
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent

We study the statistical and computational complexities of the Polyak step size gradient descent algorithm under generalized smoothness and Lojasiewicz conditions of the population loss function, namely, the limit of the empirical loss function when the sample size goes to infinity, and the stability between the gradients of the empirical and population loss functions, namely, the polynomial growth on the concentration bound between the gradients of sample and population loss functions. We demonstrate that the Polyak step size gradient descent iterates reach a final statistical radius of convergence around the true parameter after logarithmic number of iterations in terms of the sample size. It is computationally cheaper than the polynomial number of iterations on the sample size of the fixed-step size gradient descent algorithm to reach the same final statistical radius when the population loss function is not locally strongly convex. Finally, we illustrate our general theory under three statistical examples: generalized linear model, mixture model, and mixed linear regression model.
Learning Prototype-oriented Set Representations for Meta-Learning

Learning from set-structured data is a fundamental problem that has recently attracted increasing attention, where a series of summary networks are introduced to deal with the set input. In fact, many meta-learning problems can be treated as set-input tasks. Most existing summary networks aim to design different architectures for the input set in order to enforce permutation invariance. However, scant attention has been paid to the common cases where different sets in a meta-distribution are closely related and share certain statistical properties. Viewing each set as a distribution over a set of global prototypes, this paper provides a novel prototype-oriented optimal transport (POT) framework to improve existing summary networks. To learn the distribution over the global prototypes, we minimize its regularized optimal transport distance to the set empirical distribution over data points, providing a natural unsupervised way to improve the summary network. Since our plug-and-play framework can be applied to many meta-learning problems, we further instantiate it to the cases of few-shot classification and implicit meta generative modeling. Extensive experiments demonstrate that our framework significantly improves the existing summary networks on learning more powerful summary statistics from sets and can be successfully integrated into metric-based few-shot classification and generative modeling applications, providing a promising tool for addressing set-input and meta-learning problems.
Uniform Guarded Fragments

Abstract In this paper we prove that the uniform one-dimensional guarded fragment, which is a natural polyadic generalization of guarded two-variable logic, has the Craig interpolation property. We will also prove that the satisfiability problem of uniform guarded fragment is NExpTime -complete.
Knowledge and Skills Needed to Craft Successful Cybersecurity Strategies

Daily advancing technologies and next-generation networks are creating entirely dierent digital environments for people, organizations, and governments within the next several years. Because cybersecurity provision in such environments involves many actors and must overcome many evolving threats and challenges, strategies must be responsive and multi-pronged. Development and execution of suficiently savvy strategies to face the complex problems in this context necessitate identification of all the actors and operations that aect, directly or indirectly, on the cybersecurity of the digital ecosystems. In this study, we seek to provoke thinking about how actors and stakeholders could get better at crafting successful cybersecurity strategies, and identify and integrate specic types of skills required to formulation these strategies taking into account where decisions are actually made. This work provides an insight into cybersecurity education, calibrating and differentiating knowledge and skills to make the right demands on the right actors who have the authority and responsiveness to introduce change from multiple entry points. This enables practitioners to adopt more hands-on approaches that can be helpful to improve transparency, accountability and collaboration across levels of a socio-technical system.
FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning

Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years. The majority of existing works focus on supervised learning (SL) problems where it is assumed that clients carry labeled datasets while the server has no data. However, in realistic scenarios, clients are often unable to label their data due to the lack of expertise and motivation while the server may host a small amount of labeled data. How to reasonably utilize the server labeled data and the clients' unlabeled data is thus of paramount practical importance. In this paper, we propose a new FL algorithm, called FedSEAL, to solve this Semi-Supervised Federated Learning (SSFL) problem. Our algorithm utilizes self-ensemble learning and complementary negative learning to enhance both the accuracy and the efficiency of clients' unsupervised learning on unlabeled data, and orchestrates the model training on both the server side and the clients' side. Our experimental results on Fashion-MNIST and CIFAR10 datasets in the SSFL setting validate the effectiveness of our method, which outperforms the state-of-the-art SSFL methods by a large margin.
Causal discovery from conditionally stationary time-series

Causal discovery, i.e., inferring underlying causal relationships from observational data, has been shown to be highly challenging for AI systems. In time series modeling context, traditional causal discovery methods mainly consider constrained scenarios with fully observed variables and/or data from stationary time-series. We develop a causal discovery approach to handle a wide class of non-stationary time-series that are conditionally stationary, where the non-stationary behaviour is modeled as stationarity conditioned on a set of (possibly hidden) state variables. Named State-Dependent Causal Inference (SDCI), our approach is able to recover the underlying causal dependencies, provably with fully-observed states and empirically with hidden states. The latter is confirmed by experiments on synthetic linear system and nonlinear particle interaction data, where SDCI achieves superior performance over baseline causal discovery methods. Improved results over non-causal RNNs on modeling NBA player movements demonstrate the potential of our method and motivate the use of causality-driven methods for forecasting.
A Data Mining Analysis of Covid 19 Cases in States of USA

Epidemic diseases can be extremely dangerous with its hazarding influences. They may have negative effects on economies, businesses, environment, humans and workforce. In this paper, factors that are interrelated with Covid-19 pandemic have been examined using data mining methodologies and approaches. As a result of the analysis some rules and insights have been discovered and performances of the data mining algorithms have been evaluated.
Attacking Open-domain Question Answering by Injecting Misinformation

With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over misinformation-polluted contexts to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the sensitivity of open-domain QA models to corpus pollution with misinformation documents. We curate both human-written and model-generated false documents that we inject into the evidence corpus of QA models and assess the impact on the performance of these systems. Experiments show that QA models are vulnerable to even small amounts of evidence contamination brought by misinformation, with large absolute performance drops on all models. Misinformation attack brings more threat when fake documents are produced at scale by neural models or the attacker targets hacking specific questions of interest. To defend against such a threat, we discuss the necessity of building a misinformation-aware QA system that integrates question-answering and misinformation detection in a joint fashion.
IoT Based Gesture-Controlled Surveillance Rover

AbstractThe design, assembly, and working of the proposed model, “IoT based gesture-controlled surveillance rover” aims to provide a better result and communication between the operator and the communication in either of the aimed use; aid in post-disaster and surveillance. The cameras are mounted inside the chassis of the rover, giving out live stream at a specific link while being connected to the Internet to receive movement commands from the operator. Furthermore, implementation of such system would make the rescue process much accurate and quick. The rescue team would know the position of any living being under the debris through the thermal imaging from the rover. The rover is controlled by the gestures of our hand, and we get live thermal imaging at our place by using IoT. Results show that the model is working on multiple terrains with a constraint like Internet connectivity and speed is directly proportional to the livestream and control of the rover and high-power consumption.KeywordsIoTHand gesture recognitionSurveillanceThermal imagingDisaster management
Methodological principles of studying the variability of mineral properties in the subsurface

Inhomogeneities in the structure of the subsoil are manifested at any scale of research, from the heterogeneous structure of a rock fragment to the structure of the earth’s shells. The object of research is minerals in the subsurface. The subject of the study is the natural heterogeneities of a demanded mineral, which provide reliable knowledge about a mineral object, and the goal is to improve the principles of studying industrially valuable accumulations of minerals according to four most important indicators - intensity, extensiveness, accuracy and reliability, which provide a choice of methods for geological and economic assessment of the mineral object. The research methodology is based on the analysis of the principles and methods of the stage-systemic results of geological exploration of various types and industrial types of deposits, takes into account the influence of the geological structure on the characteristics of the spatial variability of the properties of minerals in the subsoil, and makes it possible to increase the reliability of the research results. To clarify the natural heterogeneity, variability of mineral bodies, the methods of modern mathematics, probability theory, and mathematical statistics are used. Methodological principles and methods make it possible to simplify the research process, reduce labour costs, time and funds. The quantitative characteristics of the variability of the most important properties of minerals solve many questions of the methodology of prospecting, exploration, testing and geological and economic assessment of the deposit. They also determine the appropriate degree of study of the deposit for its industrial development, the group of complexity of the geological structure of the deposit, and determine the volume of physical research. The article demonstrates rational methods and practical techniques for using heterogeneous sample information to establish reliable parameters of minerals. They are applicable at the stage of appraisal and exploration of deposits of ore and non-metallic minerals
Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships

Knowledge Graph (KG) embeddings provide a low-dimensional representation of entities and relations of a Knowledge Graph and are used successfully for various applications such as question answering and search, reasoning, inference, and missing link prediction. However, most of the existing KG embeddings only consider the network structure of the graph and ignore the semantics and the characteristics of the underlying ontology that provides crucial information about relationships between entities in the KG. Recent efforts in this direction involve learning embeddings for a Description Logic (logical underpinning for ontologies) named EL++. However, such methods consider all the relations defined in the ontology to be one-to-one which severely limits their performance and applications. We provide a simple and effective solution to overcome this shortcoming that allows such methods to consider many-to-many relationships while learning embedding representations. Experiments conducted using three different EL++ ontologies show substantial performance improvement over five baselines. Our proposed solution also paves the way for learning embedding representations for even more expressive description logics such as SROIQ.
The mathematical foundation of a software package for assessing the probability of a viral infection in massively occupied buildings

Текущая пандемия COVID-19 показала, что у принимающих решения органов власти отсутствуют инструменты, которые бы позволяли принимать обоснованные решения по введению карантинных мер, проведению/отмене массовых мероприятий в зданиях. Зачастую это приводит к введению либо избыточных (что вызывает ухудшение экономической ситуации), либо недостаточных мер (что приводит к ухудшению эпидемиологической ситуации). В настоящей статье дано описание математической основы и базовых принципов создания программного комплекса для оценивания вероятности заражения вирусной инфекцией, передающейся воздушно-капельным путем, в зданиях с массовым пребыванием людей The current COVID-19 pandemic has shown that decision-making authorities lack tools that would allow them to make informed decisions on the introduction of quarantine measures related to either holdingor canceling mass events in buildings. Often this leads either to the introduction of excessive (which leads to a worsening of the economic situation) or insufficient measures (which leads to a worsening of the epidemiological situation). This article describes the mathematical basic principles aimed at creating a software package for assessing the likelihood of contracting a viral infection transmitted by airborne droplets in massively occupied buildings. Evaluation of the likelihood of COVID-19 infection in public places is possible based on a joint analysis of the results of the people movement simulation, air circulation and the spread of aerosols from a carrier of the infection, taking into account the applied methods of protection (masks, ventilation). It is required to develop methods for assessing the danger of COVID-19 infection from a carrier of the virus and the methods of protection used (masks, ventilation) in specific public places. Simultaneously, the movement of people in accordance with the mode of operation, air movement (including ventilation systems) and the spread of respiratory aerosols needs to be accounted for
Kepemimpinan Kepala Sekolah dalam Pengelolaan Ekstrakurikuler di MI Nahdatul Ulama Sumber Agung

Extracuricular is needed by the education unit as a forum to develop the interests and talents of learners. The purpose of this study is how the role of the principal's leadership in the sustainability of extracurricular management at MI Nahdatul Ulama Sumber Agung. The research methods used in this study are qualitative, data sources through interviews, observations, and documentation. The results of this study are 1) The process of managing the cocurricular by the principal at MI Nahdatul Ulama Sumber Agung is carried out from the formulation, formulation, utilization, to the stage of evaluation and control of activities. 2) The obstacles faced in the extracurricular management of MI Nahdatul Ulama Sumber Agung are the difficulty of getting competent supervisors according to the field, sometimes difficulty to get infrastructure, and also obstacles in motivating learners to continue to be excited to follow the activities. 3) The result of the principal's leadership in managing extracurriculars at MI Nahdatul Ulama Sumber Agung is the interest and talent of students can be included in the race.