Reviewerly/exHarmony huggingface dataset:
How Does the Digital Transformation of Banks Improve Efficiency and Environmental, Social, and Governance Performance?

In the era of the digital economy, traditional industries have begun to realize digital transformations. For commercial banks, digital transformation is a trend and a requirement and is the only way to achieve sustainable development. At the same time, at the helm of the enterprise, executives play an essential role in the development of commercial banks. This study explored the relationship between digital bank transformation and bank efficiency, environment, society, and corporate governance (ESG) through empirical analysis, and how executives’ innovation awareness and executive technical background affect the relationships between digital bank transformation, bank efficiency, and ESG. This study used the regression method of fixed effects to conduct empirical research on the data of China’s A-share listed banks from 2011 to 2021. The research results show that the digital transformation of banks has improved efficiency and promoted the ESG performance of commercial banks. At the same time, executives’ innovation consciousness and technical background have played a positive regulatory role in banks’ digital transformation to promote bank efficiency and ESG. The main research object of this study was Chinese commercial banks. The bank’s digital transformation results were examined and the research was expanded to digital transformation and ESG. At the same time, this study has particular significance for investors who have a financial interest in banks.
Escaping Detrimental Interactions with Microwave-Dressed Transmon Qubits

Superconducting transmon qubits with fixed frequencies are widely used in many applications due to their advantages of better coherence and less control lines compared to the frequency tunable qubits. However, any uncontrolled interactions with the qubits such as the two-level systems could lead to adverse impacts, degrading the qubit coherence and inducing crosstalk. To mitigate the detrimental effect from uncontrolled interactions between qubits and defect modes in fixed-frequency transmon qubits, we propose and demonstrate an active approach using an off-resonance microwave drive to dress the qubit and to induce the ac-Stark shift on the qubit frequency. We show experimentally that the qubit frequency can be tuned well away from the defect mode so that the impact on qubit coherence is greatly reduced while maintaining the universal controls of the qubit initialization, readout, and single-qubit gate operations. Our approach provides an effective way for tuning the qubit frequency and suppressing the detrimental effect from the defect modes that happen to be located close to the qubit frequency.
Universal Machine-Learning Processing Pattern for Computing in the Video-Oculography

In this article, we present a processing pattern dedicated to video-oculography. It is a complete solution that allows for checking the external conditions accompanying the video oculography, conducting oculometric measurements based on different test models, estimating eye movement (EM) angles and detecting EM type in the stream of coordinates, and calculating its parameters. We based our architecture on neural networks (NN), machine-learning (ML) algorithms of different types, parallel/asynchronous computing, and compilations of the models, to achieve real-time processing in oculometric tests. Oculometric tests provide significant insight into central neuro-motor states, but machine-learning methods are needed to estimate their meaning. A limitation of this cognitive-analytical trend was the reliance on dedicated measuring devices, such as eye-trackers, which are usually separate and expensive equipment. Presented approach goes beyond these limitations and was developed to use standard home computer equipment, with an internet connection and computer camera. Our set of dedicated algorithms embedded in the software compensates for hardware limitations. We tested the results of the presented solution on reflexive saccades (RS) and a standard 30 frames per second (FPS) web-camera, and we were able to distinguish between young and old persons and between healthy and prodromal neurodegenerative (ND) subjects by analyzing RS parameters. Visual processes are connected to many brain structures and, by using ML methods, we are trying to dissect them. The development of solutions like the one presented in this article gives hope for the general availability of screening tests connected to ND risk and for collecting extensive data outside the laboratory. We hope that this direction will contribute to the development of ND analytic means in computational health and consequently, to the faster development of new ND preventive measures.
Health Insurance Fraud Detection Using Feature Selection and Ensemble Machine Learning Techniques

Frauds existing in the healthcare system have led to additional expenses as well as caused degradation of the quality of services being provided to patients. In this research, we have proposed a Sequential Forward Selection (SFS) method and SMOTE oversampling approach to healthcare insurance fraud detection. We employed K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Gradient Boosting Machine (GBM), Bagging classifier, and stacking meta-estimator for the classification purpose. We preferred Stacking aggregator over others in fusion with SFS as there is a statistically significant difference between them. This work categorizes, compares, and summarizes almost all published technical and review articles in healthcare fraud detection over the past 10 years. It defines the professional fraudster, formalizes the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. The accuracy is best obtained when feature selection is applied to the Stacking classifier which is 97.19%. To demonstrate the effectiveness of the proposed methodology, we have experimentally evaluated our proposition using a real-world healthcare insurance fraud dataset taken from the literature.
Trajectory-Free Motion Planning of an Unmanned Surface Vehicle Based on MPC and Sparse Neighborhood Graph

Unmanned Surface Vehicles (USV) have gained significant attention in military, science, and research applications in recent years. The development of new USV systems and increased application domain of these platforms has necessitated the development of new motion planning methods to improve the autonomy level of USVs and provide safe and robust navigation across unpredictable marine environments. This study proposes a feedback motion planning and control methodology for dynamic fully-and under-actuated USV models built on the recently introduced sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC). This approach employs a feedback motion planning strategy based on sparsely connected obstacle-free regions and the sequential composition of MPC policies. The algorithm generates a sparse neighborhood graph consisting of connected rectangular zones in the discrete planning phase. Inside each node (rectangular region), an MPC-based online feedback control policy funnels the USV with nonlinear dynamics from one rectangle to the other in the network, ensuring no constraint violation on state and input variables occurs. We systematically test the proposed algorithms in different simulation scenarios, including an extreme actuator noise scenario, to test the algorithm’s validity, effectiveness, and robustness.
A Novel RGB Image Obfuscation Technique Using Dynamically Generated All Order-4 Magic Squares

Plethora of image encryption schemes exist in literature based on the construct of magic square for realizing the purpose of image obfuscation.This magic square carries out the scrambling project of the encryption.In these schemes, normally single and static magic square is implied.To render greater scrambling effects, this study proposes a novel image encryption scheme using all order-4 magic squares whose frequency reaches to the tune of 880.These magic squares have been dynamically selected to carry out the scrambling project.As the color image is input, it is broken into its gray scale red, green and blue components.These components are joined together to make a big gray scale image.Intertwining logistic map (ILM) has been used for the generation of random data.Besides, one more stream has been created through the arithmetic manipulation of the generated three streams.Streams generated by ILM has been used to realize the effects of confusion and diffusion.First and second streams out of the four streams randomly select the address from the big gray scale image to apply the randomly selected magic square by the third stream, in order to create the scrambling effects.The fourth and last stream of random numbers is used to create the diffusion effects in the scrambled image.Plaintext senstivity has been introduced by tempering the one initial value of the chaotic system through the usage of a characteristic of the given input color image.The experimentation and security analyses sections vividly demonstrate the strength, immunity from the diverse attacks and prospects for the real world application of the proposed image cipher.In particular, we got very promising stats of information entropy (7.9974) and computational time (0.9865 seconds).No doubt, they suggest the potential application of the proposed image cipher in some real world setting.
A Blockchain-Based E-Commerce Reputation System Built With Verifiable Credentials

Reviews and reputation scores of sellers play an important role in decision-making process of potential buyers in an e-commerce system.A trustworthy and reliable reputation system is a crucial component in the e-commerce ecosystem, as buyers rely on it to make informed decisions.In this work, we propose a privacy-preserving decentralized reputation system designed to include countermeasures against some known attacks.Our model is built on two permissioned blockchains, namely Hyperledger Indy and Hyperledger Fabric.Hyperledger Indy provides an infrastructure for implementing verifiable credentials with Zero Knowledge Proof support, which is essential for privacy preservation, while Hyperledger Fabric is a robust platform for implementing smart contracts.One of the key advantages of the proposed approach is the use of verifiable credentials for digital identities of sellers, feedback tokens issued to buyers after performing an e-commerce transaction and discount tokens issued to buyers after feedback submission.This helps to ensure that the feedback and identity information is authentic and tamper-proof, reducing the likelihood of identity-related attacks.Additionally, the collection of feedbacks and application of business rules are implemented as smart contracts on Hyperledger Fabric blockchain.This provides a secure and transparent mechanism for processing feedback, reducing the likelihood of unfair feedbacks.Overall, the proposed approach presents a robust reputation system that can help reduce identity-related attacks and unfair feedbacks.The privacy-preserving nature of the system ensures that sensitive information is protected while still enabling the verification of digital identities.The use of feedback and discount tokens incentivizes buyers to provide accurate and honest feedback, which can help reduce unfair feedbacks and identity-related attacks.Finally, the use of smart contracts ensures transparency and immutability, which enhances the overall reliability of the system.
Forecasting the GDP in the United States by Using ARIMA Model

This paper aims to give an overall view of the Gross Domestic Product (GDP) in the United States and determine the optimal model to predict the growth of GDP by using the Autoregressive Integrated Moving Average Model (ARIMA). The ARIMA model was performed for 93 years from 1929 to 2022 of Gross Domestic Product, Billions of Dollars, Annually from Federal Reserve Economic Data (https://fred. stlouisfed.org). The researcher conclude that the estimated model of the first-order difference for the logarithm of GDP (DLGDP) series is ARIMA (1,1,1) with coefficients: C = 0.057064, AR (1) = 0.489046 &amp; MA (1) = 0.265583 where S.E. of regression equals 0.051529, R-squared value is about 0.412974, Durbin-Waston statistic (1.961008) and the probability of F-statistic equals (0.000000), which gives the forecast value 0.10436 of LGDP in 2022, while the actual value equals 0.8818 with very low relative error 1.617%, therefore, the forecast value is close to the actual value and indicates that the ARIMA (1,1,1) model has a good fitting effect.
Subgroup Discovery in Machine Learning Problems with Formal Concepts Analysis and Test Theory Algorithms

A number of real-world problems of automatic grouping of objects or clustering require a reasonable solution and the possibility of interpreting the result. More specific is the problem of identifying homogeneous subgroups of objects. The number of groups in such a dataset is not specified, and it is required to justify and describe the proposed grouping model. As a tool for interpretable machine learning, we consider formal concept analysis (FCA). To reduce the problem with real attributes to a problem that allows the use of FCA, we use the search for the optimal number and location of cut points and the optimization of the support set of attributes. The approach to identifying homogeneous subgroups was tested on tasks for which interpretability is important: the problem of clustering industrial products according to primary tests (for example, transistors, diodes, and microcircuits) as well as gene expression data (collected to solve the problem of predicting cancerous tumors). For the data under consideration, logical concepts are identified, formed in the form of a lattice of formal concepts. Revealed concepts are evaluated according to indicators of informativeness and can be considered as homogeneous subgroups of elements and their indicative descriptions. The proposed approach makes it possible to single out homogeneous subgroups of elements and provides a description of their characteristics, which can be considered as tougher norms that the elements of the subgroup satisfy. A comparison is made with the COBWEB algorithm designed for conceptual clustering of objects. This algorithm is aimed at discovering probabilistic concepts. The resulting lattices of logical concepts and probabilistic concepts for the considered datasets are simple and easy to interpret.
Harmonic-Net: Fundamental Frequency and Speech Rate Controllable Fast Neural Vocoder

There is a need to improve the synthesis quality of HiFi-GAN-based real-time neural speech waveform generative models on CPUs while preserving the controllability of fundamental frequency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> ) and speech rate (SR). For this purpose, we propose Harmonic-Net and Harmonic-Net+, which introduce two extended functions into the HiFi-GAN generator. The first extension is a downsampling network, named the excitation signal network, that hierarchically receives multi-channel excitation signals corresponding to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> . The second extension is the layerwise pitch-dependent dilated convolutional network (LW-PDCNN), which can flexibly change its receptive fields depending on the input <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> to handle large fluctuations in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> for the upsampling-based HiFi-GAN generator. The proposed explicit input of excitation signals and LW-PDCNNs corresponding to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> are expected to realize high-quality synthesis for the normal and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> -conversion conditions and for the SR-conversion condition. The results of experiments for unseen speaker synthesis, full-band singing voice synthesis, and text-to-speech synthesis show that the proposed method with harmonic waves corresponding to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> can achieve higher synthesis quality than conventional methods in all (i.e., normal, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$f_{\mathrm{o}}$</tex-math></inline-formula> -conversion, and SR-conversion) conditions.
Initial insight into three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles

Abstract While data sharing has received research interest in recent times, its real status remains unclear, owing to its ambiguous concept. To understand the current status of data sharing, this study examined primary reuse, data integration, and dataset release as the actual practices of data sharing. A total of 963 articles, chosen from those published in 2018 and registered in the Web of Science global citation database, were manually checked. Existing data were reused in the mode of data integration (13.3%) as frequently as they were for the mode of primary reuse (12.1%). Dataset release was the least common mode (9.0%). The results show the variation in data sharing and indicate the need for standardization of data description in articles based on thorough registration and expansion in public data archives to close the loop that results in the virtuous cycle of research data.
TMNet: A Two-Branch Multi-Scale Semantic Segmentation Network for Remote Sensing Images

Pixel-level information of remote sensing images is of great value in many fields. CNN has a strong ability to extract image backbone features, but due to the localization of convolution operation, it is challenging to directly obtain global feature information and contextual semantic interaction, which makes it difficult for a pure CNN model to obtain higher precision results in semantic segmentation of remote sensing images. Inspired by the Swin Transformer with global feature coding capability, we design a two-branch multi-scale semantic segmentation network (TMNet) for remote sensing images. The network adopts the structure of a double encoder and a decoder. The Swin Transformer is used to increase the ability to extract global feature information. A multi-scale feature fusion module (MFM) is designed to merge shallow spatial features from images of different scales into deep features. In addition, the feature enhancement module (FEM) and channel enhancement module (CEM) are proposed and added to the dual encoder to enhance the feature extraction. Experiments were conducted on the WHDLD and Potsdam datasets to verify the excellent performance of TMNet.
Integrated preference argumentation and applications in consumer behaviour analyses

Consumer preference studies in economics rest heavily on the behavioural interpretation of preference especially in the form of Revealed Preference Theory (RPT). Viewing purchasing decisions as a kind of human reasoning, in this paper we are interested in generalising behaviourism to preference-based argumentation where existing frameworks are arguably governed by the opposing mentalistic interpretation of preference. Concretely our contributions are three-fold. First we re-construct and unify two main approaches to RPT in argumentation terms by developing a Revealed Preference Argumentation (RPA) framework. We show that key RPT-based consumer analyses such as different rationality checks of a consumer behaviour and various kinds of such behaviour's extrapolations can be translated to computational tasks in RPA. RPA is in turn subsumed by the second contribution - an Integrated Preference Argumentation (IPA) framework which fully integrates mentalism and behaviourism. In particular we show that RPA is just a special case of IPA with only revealed preference, while existing preference-based argumentation frameworks can be viewed as IPA frameworks with only stated preference. As the third contribution, we develop a complete set of IPA algorithms and establish their correctness and termination for a general class of IPA frameworks. For demonstration we implement the algorithms in Prolog to obtain an IPA reasoning engine and test the engine with various RPT-based consumer behaviour analyses. In sum, it is argued that the current paper provides not only a theory of Integrated Preference Argumentation, but also a related development tool for future applications of argumentation to behavioural economics - a largely unexplored area so far though Dung used the stable marriage problem in microeconomic game theory to partly motivate the argumentation theory.
An Overview of the PAKF-JPDA Approach for Elliptical Multiple Extended Target Tracking Using High-Resolution Marine Radar Data

Ground radar stations observing specific regions of interest nowadays provide detections in the form of point-clouds. This article focuses on a framework that consists of an elliptical multitarget tracker, referred to as Principal-Axes based Kalman Filter (PAKF)-based Joint Probabilistic Data Association (JPDA) (PAKF-JPDA), to enable maritime traffic monitoring. The framework touches on two major stages, target detection and target tracking. For the former, we employed a clustering approach and for the latter, we presented a data-association-based version of the PAKF tracker with an automatic track management functionality. The framework’s benefits are demonstrated when it is applied to the radar streaming in a harbor setting based on a homogeneous multisensor tracking system by comparing our results against their corresponding reference data with visualizations, including performance measures.