Reviewerly/exHarmony huggingface dataset:
Extreme Gradient Boost Classifier based Credit Card Fraud Detection Model

Financial fraud has evolved into an extremely serious problem in the business world. The effects of financial fraud on businesses, the economy, and people’s standard of living are widespread. Credit card fraud describes the shady practice of using personal information for online purchases. As the use of credit cards grows, so does the prevalence of fraud involving these payments. These days, credit card transactions are commonplace not only for online but also for in-store purchases. However, it has also made it easier for scammers to take advantage of this great opportunity. We can identify fraudulent transactions using Credit Card Fraud Detection. To handle imbalanced data, the proposed model in this work employs the XGBoost classifier to detect fraud transactions. The typical technique pre-determines the threshold value, resulting in inefficiency. Thus, in our proposed approach, several threshold values are computed and compared to identify the ideal value that provides an optimal outcome and high efficiency.
Elicitation of Content Layout Preferences in Virtual 3D Spaces Based on a Free Layout Creation Task

Three-dimensional virtual reality (VR) environments, whether operating on desktop platforms or immersive screens, have been recognized for enabling novel and extremely engaging methods of interacting with digital content across various fields of application. Studies conducted over the past several years have also consistently suggested that utilizing 3D in contrast to 2D interfaces can lead to enhancements in multiple performance dimensions. These enhancements encompass better understanding and retention of information, increased capacity for inventive and efficient collaboration, and the ability to execute workflows that integrate numerous information sources more quickly. At the same time, how digital content such as documents, audio–visual content and web browsers are integrated into 3D spaces is often decided by the creators of the spaces based on either aesthetic considerations, or on a case-by-case basis depending on the workflow. In this paper, we present the results of an experiment we conducted to better understand how users prefer to arrange digital content in their 3D environments, depending on the subject matter, the format of the content (e.g., text-based, image, or audio–visual) and the 3D objects within the space. The results of the experiment presented in the paper can help inform future 3D VR design methodologies and may also provide support for automated content arrangement solutions.
Anomaly Detection in Image Processing using Artificial Intelligence

Any method that looks for samples that deviate from anticipated patterns is sometimes referred to as anomaly detection. Many outlier finding methods are established on the types of irregularities, various types of data labels that are available, and the applications. The aim of this paper is to give an overview and systematic evaluation of outlier finding research. We believe that the paper contributes to a better understanding of the numerous fields in various types of research done on this subject. The approaches for anomaly detection have been separated into many groups. We discuss the pros and cons of each category and look at how computationally complex the various methods are in terms of real-world application domains. We concluded the paper by providing research gaps and constraints we faced while applying artificial intelligence algorithms in practical applications. This review paper examines several outlier detection and treatment techniques in images, as well as gives better performance of various AI approaches.
Computerized Face Mask Detection System Using Deep CNN and Transfer Learning

Since the COVID-19 pandemic is on the rise again with hazardous effects in China, it has become very crucial for global individuals and the authorities to avoid spreading of the virus. This research aims to identify algorithms with high accuracy and moderate computing complexity at the same time (although conventional machine learning works on low computation power, we have rather used CNN for our research work as the accuracy of CNN is drastically greater than the former), to identify the proper enforcement of face masks. In order to find the best Neural Network architecture we used many deep CNN Methodologies to solve classification problem in regards of masked and non masked image dataset. In this approach we applied different model architectures, like VGG16, Resnet50, Resnet101 and VGG19, on a large dataset to train on and compared the model on the basis of accuracy in which VGG16 came out to be the best. VGG16 was further tuned with different optimizers to determine the one best fit of the model. VGG16 gave an ideal accuracy of 99.37% with the best fit optimizer over a real life data set.
Map-in-Parallel-Coordinates Plot (MPCP): Field Trial Studies of High-Dimensional Geographical Data Analysis

As the world has become increasingly digitalized in recent years, high-dimensional data with geographical location coordinate attributes, mainly referring to latitude and longitude, have been accumulated and spread to many disciplines. It is challenging to analyze such data. The map-in-parallel-coordinates plot (MPCP) is an incorporate visual analysis method that can express, filter, and highlight high-dimensional geographical data to facilitate data exploration and comprehension. In this paper, the MPCP underwent a series of field trial studies to verify its applicability, adaptability, and high efficacy in the real-world. The results of the evaluation were positive, which provides reasonable proof and new insights into the benefits of using MPCP to visually analyze high-dimensional geographical datasets.
Spatio-Trajectorial Optical Flow for Higher-Order Deformation Analysis in Solid Experimental Mechanics

Material models are required to solve continuum mechanical problems. These models contain parameters that are usually determined by application-specific test setups. In general, the theoretically developed models and, thus, the parameters to be determined become increasingly complex, e.g., incorporating higher-order motion derivatives, such as the strain or strain rate. Therefore, the strain rate behaviour needs to be extracted from experimental data. Using image data, the most-common way in solid experimental mechanics to do so is digital image correlation. Alternatively, optical flow methods, which allow an adaption to the underlying motion estimation problem, can be applied. In order to robustly estimate the strain rate fields, an optical flow approach implementing higher-order spatial and trajectorial regularisation is proposed. Compared to using a purely spatial variational approach of higher order, the proposed approach is capable of calculating more accurate displacement and strain rate fields. The procedure is finally demonstrated on experimental data of a shear cutting experiment, which exhibited complex deformation patterns under difficult optical conditions.
Privacy-Preserving Medical Data-Sharing System with Symmetric Encryption Based on Blockchain

Nowadays, data between hospitals are usually not interoperable, which brings great inconvenience to medical data sharing and patients’ medical treatment. In addition, patients do not want their medical data to be leaked during the sharing process. Researchers have employed blockchain to build data-sharing systems to address these issues. However, current systems do not restrict the power of participants, nor do they prevent visitors from sharing the obtained data to unauthorized parties. To address these issues, we propose a private data-sharing system with symmetric encryption for the medical industry that implements power restriction and access control, and prevents the leakage of private data. To be specific, firstly, symmetric encryption algorithm is utilized to encrypt medical data to protect the privacy of data owner. Secondly, our proposed system is built on a new blockchain framework, in which only visitors with permission can access the medical data. Thirdly, we employ chameleon signature to prevent visitors from sharing data with other parties without permission. Finally, we make the power of participants in the system revocable to prevent them from abusing their power. Our proposed system has been proven to be secure through security analysis and can protect the privacy of patients. In addition, the experimental results show that our system has excellent performance in terms of time overhead compared to other systems.
Optimal storage capacity of quantum Hopfield neural networks

Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here quantum generalizations of classical networks realizing associative memories---capable of retrieving patterns, or memories, from corrupted initial states---have been proposed. It is a challenging open problem to analyze quantum associative memories with an extensive number of patterns and to determine the maximal number of patterns the quantum networks can reliably store, i.e., their storage capacity. In this work, we propose and explore a general method for evaluating the maximal storage capacity of quantum neural network models. By generalizing what is known as Gardner's approach in the classical realm, we exploit the theory of classical spin glasses for deriving the optimal storage capacity of quantum networks with quenched pattern variables. As an example, we apply our method to an open-system quantum associative memory formed of interacting spin-1/2 particles realizing coupled artificial neurons. The system undergoes a Markovian time evolution resulting from a dissipative retrieval dynamics that competes with a coherent quantum dynamics. We map out the nonequilibrium phase diagram and study the effect of temperature and Hamiltonian dynamics on the storage capacity. Our method opens an avenue for a systematic characterization of the storage capacity of quantum associative memories.
SECURING CORPORATE <scp>Io</scp>T DEVICES: CHALLENGES, STRATEGIES, AND THE ROLE OF AI AND ML IN CYBERSECURITY

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).
Multi-label Text Classification Model Based on Multi-level Constraint Augmentation and Label Association Attention

In the multi-label text classification task, a text usually corresponds to multiple label categories, and the labels have correlation and hierarchical structure. However, when the label hierarchy is unknown, the number of various labels is not balanced, which makes it difficult for the model to classify low-frequency labels. At the same time, due to the existence of similar labels, the model will be difficult to distinguish similar labels. In this paper, we propose a multi-label text classification model based on multi-level constraint augmentation and label association attention. Compared with traditional methods, our method has two contributions: (1) In order to alleviate the problem of unbalanced number of different label categories and ensure the rationality of sample generation, we propose a data augmentation method based on multi-level constraints. In the process of sample generation, this method uses historical generation information, sample original text information and sample topic to constrain the generated text. (2) In order to make the model recognize the associated labels accurately, we propose an interaction mechanism based on label association attention and filter gate. This method combines text information and label weight information. At the same time, our classification model considers the important weights of text sentences and effectively utilizes the co-occurrence relationship between labels. Experimental results on three benchmark datasets show that our model outperforms state-of-the-art methods on all main evaluation metrics, especially on low-frequency label prediction with sparse samples.
Estimating phase parameters of a three-level system interacting with two classical monochromatic fields in simultaneous and individual metrological strategies

Recently, the Hilbert-Schmidt speed, as a special class of quantum statistical speed, has been reported to improve the interferometric phase in single-parameter quantum estimation. Here, we test this concept in the multiparameter scenario where two laser phases are estimated in a theoretical model consisting of a three-level atom interacting with two classical monochromatic fields. When the atom is initially prepared in the lower bare state taking into account the detuning parameters, we extract an exact analytical solution of the atomic density matrix in the case of two-photon resonant transition. Further, we compare the performance of laser phase parameters estimation in individual and simultaneous metrological strategies, and we explore the role of quantum coherence in improving the efficiency of unknown multi-phase shift estimation protocols. The obtained results show that the Hilbert-Schmidt speed detects the lower bound on the statistical estimation error as well as the optimal estimation regions, where its maximal corresponds to the maximal quantum Fisher information, the performance of simultaneous multiparameter estimation with individual estimation inevitably depends on the detuning parameters of the three-level atom, and not only the quantum entanglement, but also the quantum coherence is a crucial resource to improve the accuracy of a metrological protocol.
Improved learning performance for small datasets in high dimensions by new dual-net model for non-linear interpolation virtual sample generation

The number of reliable samples obtained in early decision-making activity is usually relatively small. Due to variable distribution and incomplete structure of tiny datasets, it is challenging to create reliable and robust predictive modeling using classic statistical and machine learning models in small sample settings. The virtual sample generation (VSG) technique improves model learning accuracies for minimal datasets across diverse applications. Virtual samples on independent variables were generated using established VSG methods predicated on the assumption of a probability distribution or a membership function to fill data gaps. However, in the actual world, non-linear function interactions between variables are common. To address this issue, this paper developed a novel VSG method called Dual-VSG, which generates non-linear interpolation virtual samples using a self-supervised learning (SSL) framework to improve learning performance on small datasets. We generated non-linear interpolation virtual samples without labels by estimating non-linear functions and transforming them into a high-dimensional space using the proposed dual-net model. The weights of the dual-net model are transferred to a downstream task to generate virtual sample labels. To demonstrate the effectiveness of the suggested strategy, this research employed five datasets. On the Backpropagation Neural Networks (BPNN) predictive model, we compared the suggested method's prediction performance to two state-of-the-art VSG approaches. To assess prediction performance on a regression dataset, the Mean Absolute Percentage Error (MAPE) and the Root Mean Square Error (RMSE) are used. Furthermore, the classification accuracy (ACC) and the Fl measure are used to assess classification capability on classification datasets. In addition, the paired t-test was utilized to see if the suggested Dual-VSG approach differed significantly from the other VSG methods in terms of RMSE, MAPE, accuracy (ACC), or F1 score. For short datasets, the suggested Dual-VSG method outperforms those VSG methods, according to our experimental results.
Future of Public Sector Enterprises in the Metaverse

This chapter explores the future of public sector enterprises in the contemporary metaverse technological sphere. A literature review approach was adopted to explore customer expectations, sustainable governance forms, and innovations in the metaverse ecosystem. Findings indicate that most of the technologies that make up the metaverse's building blocks already exist or are in an advanced stage of development. Furthermore, there was a rise in the use of metaverse technology like virtual and augmented reality (AR), particularly in the healthcare sector during the pandemic. Digital currency adoption, including the use of cryptocurrencies and central bank digital currencies, was also noted. Public sector enterprises should rely on a variety of interdependent players rather than assuming they have a monopoly on knowledge or resources to govern. A process of co-evolution between non-government and governmental entities is a sine qua non for effective social innovation by public sector enterprises in the metaverse ecosystem.
INFORMATIVE COMPONENT OF MEDIATOR COMPETENCE OF FUTURE SOCIAL WORKERS

Abstract. A high level of conflictogenic potential in the context of world globalization is one of the characteristic features of modern society, which is very threatening fact. The article analyzes mediator competencethat combines professional knowledge, skills, qualification of the mediator, experience in mediation etc., which would allow making certain objective judgments and correct decisions for conflict resolution and satisfy expectations of the conflict parties. It was found that when training specialists, especially social workers, for the role of a mediator, one should take into account the goal, tasks, principles of mediation. It is necessary to form and advance their knowledge, skills and abilities related to mediation for them tobe able to provide social and legal protection of peoplebasing on an interdisciplinary approach in the field of conflictology, law, psychology, jurisprudence and personal characteristics and experience.
The Criminal Side of Cryptocurrency

As cryptocurrency (crypto) has become more and more popular, so has crypto-related crime. There has been a lack of academic research on crypto-related crime, but it is becoming more prevalent in the last couple of years. Crypto-related crime became especially significant in the impact it had on victims and the awareness of these crimes in the media and the government in late 2019 and early 2020 as various criminal organizations and criminal opportunities opened up as cryptocurrency became mainstream. The common crimes related to cryptocurrency include fraud, theft, and money laundering. In 2021 estimates of crypto-related crime were estimated to be as high as $14 billion, which is a small fraction of a percent of the cryptocurrency transactions that were around $15.8 trillion in 2021. The purpose of the chapter is to provide a detailed account of the common crypto-related crimes and scams that have occurred and to evaluate the effectiveness of enforcement of these crimes.
Blockchain Technology Use to Streamline Climate Investment

The main objective of this chapter is to contour the climate investing by implementing blockchain technology, which will lead to transparency within the network. Climate investing aids the companies that are involved in reducing carbon emissions, subsidising the usage of fossil fuels and manufacturing alternate options. This investment will be directly propositional to the “green revolution,” which the Indian government is making efforts towards. Blockchain technology will help towards this investment by creating a transparent and secured network that is impossible to hack. The database management will be designed using the latest programming techniques, which may also include artificial intelligence. Climate finance includes the financial resources which has been used to support the sustainability and climate change.
TV series as disseminators of emerging vocabulary: Non-codified expressions in the TV Corpus

Abstract This study presents a method for identifying words that appear in corpus data earlier than their first date of attestation in dictionaries. We demonstrate the application of this method based on a large diachronic corpus, the TV Corpus, and the Oxford English Dictionary (OED). Combining automatic extraction of candidate terms from the TV Corpus with comprehensive manual analysis and verification, the method identifies 32 words that were used in TV series before their first attestation in the OED. We present a detailed discussion of these words, analysing their distribution across decades and genres of the TV Corpus, their origins, semantic domains and word-formation processes. We also present extracts with their first uses in the TV Corpus and analyse how the words were presented to the large and anonymous mass audience. Our study shows that the method we present is suitable for identifying early attestations of words in large corpora, even though in the case of the TV Corpus, a great deal of manual analysis and verification is needed. In addition, we argue that TV series and other types of fictional texts are an important resource for studying the coinage and spread of terms, due to their function and the fact that they address a mass audience.
Multi-Strategy Improved Flamingo Search Algorithm for Global Optimization

To overcome the limitations of the Flamingo Search Algorithm (FSA), such as a tendency to converge on local optima and improve solution accuracy, we present an improved algorithm known as the Multi-Strategy Improved Flamingo Search Algorithm (IFSA). The IFSA utilizes a cube chaotic mapping strategy to generate initial populations, which enhances the quality of the initial solution set. Moreover, the information feedback model strategy is improved to dynamically adjust the model based on the current fitness value, which enhances the information exchange between populations and the search capability of the algorithm itself. In addition, we introduce the Random Opposition Learning and Elite Position Greedy Selection strategies to constantly retain superior individuals while also reducing the probability of the algorithm falling into a local optimum, thereby further enhancing the convergence of the algorithm. We evaluate the performance of the IFSA using 23 benchmark functions and verify its optimization using the Wilcoxon rank-sum test. The compared experiment results indicate that the proposed IFSA can obtain higher convergence accuracy and better exploration abilities. It also provides a new optimization algorithm for solving complex optimization problems.
EVOAC-HP: An Efficient and Verifiable Outsourced Access Control Scheme with Hidden Policy

As medical data become increasingly important in healthcare, it is crucial to have proper access control mechanisms, ensuring that sensitive data are only accessible to authorized users while maintaining privacy and security. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is an attractive access control solution that can offer effective, fine-grained and secure medical data sharing, but it has two major drawbacks: Firstly, decryption is computationally expensive for resource-limited data users, especially when the access policy has many attributes, limiting its use in large-scale data-sharing scenarios. Secondly, existing schemes are based on data users' attributes, which can potentially reveal sensitive information about the users, especially in healthcare data sharing, where strong privacy and security are essential. To address these issues, we designed an improved CP-ABE scheme that provides efficient and verifiable outsourced access control with fully hidden policy named EVOAC-HP. In this paper, we utilize the attribute bloom filter to achieve policy hiding without revealing user privacy. For the purpose of alleviating the decryption burden for data users, we also adopt the technique of outsourced decryption to outsource the heavy computation overhead to the cloud service provider (CSP) with strong computing and storage capabilities, while the transformed ciphertext results can be verified by the data user. Finally, with rigorous security and reliable performance analysis, we demonstrate that EVOAC-HP is both practical and effective with robust privacy protection.