Reviewerly/exHarmony huggingface dataset:
Effects of COVID-19 Pandemic on Accounting Students’ Capability to Use Technology

The study aims to examine accounting students’ perceptions of: (i) the importance of using technology during the COVID-19 pandemic; (ii) the impact of the COVID-19 pandemic on the extent of their use of technology; (iii) the impact of the COVID-19 pandemic on their capability to use technology; and (iv) their satisfaction with using the Tally software program during the COVID-19 pandemic. A quantitative method was used in which a questionnaire was distributed to students at a higher education institution; specifically, students in the Accounting and Finance Department at Gulf University. The results showed that among the responding students there was a high level of agreement on the importance of using technology during the COVID-19 pandemic. Respondents highly agreed on the significant impact of the COVID-19 pandemic on the extent of students’ use of technology. Moreover, findings indicated that there was a high level of agreement on the impact of the COVID-19 pandemic on the students’ capability to use technology. Lastly, the results pointed out a high level of satisfaction with using the Tally software program during the COVID-19 pandemic. The study contributes to the body of knowledge by providing empirical evidence on the issue of using technology in education during COVID-19. The results provide evidence that students are now more competent in using most educational technologies. Hence the study helps policymakers and educational institutions appreciate the importance of technological tools in education, and will therefore encourage them to apply technological tools that can improve student performance.
Litmus Test for a Democratic Public Sphere? Discussion on the Abortion Issue on Polish Twitter

The paper’s aim is to present key findings of the analysis of the discussion on the abortion issue on Polish Twitter. Social media are believed to be a tool serving the political periphery to participate in political discourse and with the COVID- 19 pandemic they have become crucial in our everyday communication. In this context, the Twitter discussion on abortion issue and Women’s Strike protests that took place in October 2020 in Poland was analyzed. The discussion on the abortion issue itself in many countries brings broader questions on the state of public discourse and democratic processes, and the protests brought even more inquiries about freedom of speech or the right to protest. In total, 68,716 tweets were collected from October 20 till November 15, 2020, and analyzed with MAXQDA software to answer two main research questions: RQ1: Who was shaping the discussion in the analyzed period? RQ2: Were the two sides of the conflict represented in the discussion? The analysis showed great potential that Twitter has for Polish public discourse, but also many obstacles, problems, and even social cleavages manifesting in the analyzed discussion.
A Study on the Designation Institution for Supercomputer Specialized Centers in Republic of Korea

In Korea, specialized centers are designated for 10 strategic fields for the purpose of jointly utilizing supercomputer resources at the national level.Based on the "National Supercomputing Innovation Strategy," it plans to select 10 centers in three stages by 2030, and has now completed the designation of the first-stage specialized centers in 2022.With the second designation in 2024 ahead, it is urgent to review and improve the existing designation institution for fairer and more effective selection of specialized centers.Therefore, this paper analyzed the influence of evaluation items and the influence of evaluation items on evaluation results by using logistic regression analysis and network centrality analysis to prepare improvement plans for the existing evaluation model.As a result of the analysis, improvement measures were derived, such as subdividing evaluation items with low impact, expanding the items, and lowering the allotment of evaluation items with low impact.
Explaining black-box classifiers: Properties and functions

Explaining black-box classification models is a hot topic in AI, with the overall goal of improving trust in decisions made by such models. Several works have been done and diverse functions have been proposed. However, their formal properties and links have not been sufficiently studied. This paper presents four contributions: The first consists of investigating global explanations of black-box classifiers. We provide a formal and unifying framework in which such explanations are defined from the whole feature space. The framework is based on two concepts, which are seen as two types of global explanations: arguments in favour of (or pro) predictions and arguments against (or con) predictions. The second contribution consists of defining various types of local explanations (abductive explanations, counterfactuals, contrastive explanations) from the whole feature space, investigating their properties, links and differences, and showing how they relate to global explanations. The third contribution consists of analysing and defining explanation functions that generate (global, local) abductive explanations from incomplete information (i.e., from a subset of the feature space). We start by proposing two desirable properties that an explainer would satisfy, namely success and coherence. The former ensures the existence of explanations while the latter ensures their correctness. We show that in the incomplete case, the two properties cannot be satisfied together. The fourth contribution consists of proposing two functions that generate abductive explanations and which satisfy coherence at the expense of success.
Rectifier Output Voltage [Enigmas, etc.]

Since the early 1900s, single series diode rectifiers have long been used in amplitude modulation signal detection. One may think that their design theory is already well matured. Let us check and see if that is true.
Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation

In the present study, we aimed to evaluate the performance of machine learning (ML) models for identification of acute myocardial infarction (AMI) or death within 30 days among emergency department (ED) chest pain patients.Using data from 9519 consecutive ED chest pain patients, we created ML models based on logistic regression or artificial neural networks. Model inputs included sex, age, ECG and the first blood tests at patient presentation: High sensitivity TnT (hs-cTnT), glucose, creatinine, and hemoglobin. For a safe rule-out, the models were adapted to achieve a sensitivity > 99% and a negative predictive value (NPV) > 99.5% for 30-day AMI/death. For rule-in, we set the models to achieve a specificity > 90% and a positive predictive value (PPV) of > 70%. The models were also compared with the 0 h arm of the European Society of Cardiology algorithm (ESC 0 h); An initial hs-cTnT < 5 ng/L for rule-out and ≥ 52 ng/L for rule-in. A convolutional neural network was the best model and identified 55% of the patients for rule-out and 5.3% for rule-in, while maintaining the required sensitivity, specificity, NPV and PPV levels. ESC 0 h failed to reach these performance levels.An ML model based on age, sex, ECG and blood tests at ED arrival can identify six out of ten chest pain patients for safe early rule-out or rule-in with no need for serial blood tests. Future studies should attempt to improve these ML models further, e.g. by including additional input data.
Towards a Controllable and Reversible Privacy Protection System for Facial Images through Enhanced Multi-Factor Modifier Networks

Privacy protection data processing has been critical in recent years when pervasively equipped mobile devices could easily capture high-resolution personal images and videos that may disclose personal information. We propose a new controllable and reversible privacy protection system to address the concern in this work. The proposed scheme can automatically and stably anonymize and de-anonymize face images with one neural network and provide strong security protection with multi-factor identification solutions. Furthermore, users can include other attributes as identification factors, such as passwords and specific facial attributes. Our solution lies in a modified conditional-GAN-based training framework, the Multi-factor Modifier (MfM), to simultaneously accomplish the function of multi-factor facial anonymization and de-anonymization. It can successfully anonymize face images while generating realistic faces satisfying the conditions specified by the multi-factor features, such as gender, hair colors, and facial appearance. Furthermore, MfM can also de-anonymize de-identified faces to their corresponding original ones. One crucial part of our work is design of physically meaningful information-theory-based loss functions, which include mutual information between authentic and de-identification images and mutual information between original and re-identification images. Moreover, extensive experiments and analyses show that, with the correct multi-factor feature information, the MfM can effectively achieve nearly perfect reconstruction and generate high-fidelity and diverse anonymized faces to defend attacks from hackers better than other methods with compatible functionalities. Finally, we justify the advantages of this work through perceptual quality comparison experiments. Our experiments show that the resulting LPIPS (with a value of 0.35), FID (with a value of 28), and SSIM (with a value of 0.95) of MfM demonstrate significantly better de-identification effects than state-of-the-art works. Additionally, the MfM we designed can achieve re-identification, which improves real-world practicability.
Illustrating the Value of Prior Predictive Checking for Bayesian Structural Equation Modeling

A unique feature of Bayesian estimation is the inclusion of prior knowledge through prior distributions. These prior distributions can benefit or impair many components of the ensuing analysis. Priors are especially important to assess in the context of structural equation models (SEMs), which often carry data and modeling complexities where priors can be particularly influential. In this article, we illustrate a statistical approach to assess the impact of our prior specifications: the prior predictive checking procedure. We introduce a comprehensive prior predictive checking workflow that organizes the procedure into clear steps, and we relate this workflow to the SEM framework. Through three examples, we demonstrate how the workflow can aid researchers in more fully understanding different aspects of their prior specification within SEM. Code and additional resources are provided in the online Supplemental Materials to facilitate future application of the prior predictive checking procedure within the SEM framework.
Advantages and Limitations of Quantum Routing

The swap gate is a ubiquitous tool for moving information on quantum hardware, yet it can be considered a classical operation because it does not entangle product states. Genuinely quantum operations could outperform swap for the task of permuting qubits within an architecture, which we call routing. We consider quantum routing in two models: (i) allowing arbitrary two-qubit unitaries, or (ii) allowing Hamiltonians with norm-bounded interactions. We lower bound the circuit depth or time of quantum routing in terms of spectral properties of graphs representing the architecture interaction constraints, and give a generalized upper bound for all simple connected n-vertex graphs. In particular, we give conditions for a superpolynomial classical-quantum routing separation, which exclude graphs with a small spectral gap and graphs of bounded degree. Finally, we provide examples of a quadratic separation between gate-based and Hamiltonian routing models with a constant number of local ancillas per qubit and of an Ω(n) speedup if we also allow fast local interactions.1 MoreReceived 1 July 2022Accepted 5 January 2023DOI:https://doi.org/10.1103/PRXQuantum.4.010313Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum Information
Leadership, Crisis Management, and Business Continuity

The objective of the current research was to identify the mediating role of crisis management between leadership and business continuity. Crisis management has always been considered as a responsibility of the leaders of the organizations. Moreover, leaders have to show their true leadership skills during the time of crisis in order to keep things going. For meeting the objectives of the study, the data has been collected from 384 employees working at public sector organizations of UAE. The data has been collected by applying survey techniques and self administered questionnaires were distributed using simple random sampling. The findings from structural equation modelling revealed that leadership has a significant direct impact over business continuity as well as crisis management. this shows that leaders that are efficient in leadership holds the capability to manage the crisis. Moreover, the mediating role of crisis management between leadership and business continuity is also significant.
Masked Face Recognition System Based on Attention Mechanism

With the continuous development of deep learning, the face recognition field has also developed rapidly. However, with the massive popularity of COVID-19, face recognition with masks is a problem that is now about to be tackled in practice. In recognizing a face wearing a mask, the mask obscures most of the facial features of the face, resulting in the general face recognition model only capturing part of the facial information. Therefore, existing face recognition models are usually ineffective in recognizing faces wearing masks. This article addresses this problem in the existing face recognition model and proposes an improvement of Facenet. We use ConvNeXt-T as the backbone of the network model and add the ECA (Efficient Channel Attention) mechanism. This enhances the feature extraction of the unobscured part of the face to obtain more useful information, while avoiding dimensionality reduction and not increasing the model complexity. We design new face recognition models by investigating the effects of different attention mechanisms on face mask recognition models and the effects of different data set ratios on experimental results. In addition, we construct a large set of faces wearing masks so that we can efficiently and quickly train the model. Through experiments, our model proved to be 99.76% accurate for real faces wearing masks. A combined accuracy of 99.48% for extreme environments such as too high or lousy contrast and brightness.
Observation of distinct phase transitions in a nonlinear optical Ising machine

Optical Ising machines promise to solve complex optimization problems with an optical hardware acceleration advantage. Here we study the ground state properties of a nonlinear optical Ising machine realized by spatial light modulator, Fourier optics, and second harmonic generation in a nonlinear crystal. By tuning the ratio of the light intensities at the fundamental and second harmonic frequencies, we experimentally observe two distinct ferromagnetic-to-paramagnetic phase transitions: a second-order phase transition where the magnetization changes to zero continuously and a first-order phase transition where the magnetization drops to zero abruptly as the effective temperature increases. Our experimental results are corroborated by a numerical simulation based on the Monte Carlo Metropolis-Hastings algorithm, and the physical mechanism for the distinct phase transitions can be understood with a mean-field theory. Our results showcase the great flexibility of the nonlinear optical Ising machine, which may find important potential applications in solving combinatorial optimization problems.
Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning

Discriminativeness is a desirable feature of image captions: captions should describe the characteristic details of input images. However, recent high-performing captioning models, which are trained with reinforcement learning (RL), tend to generate overly generic captions despite their high performance in various other criteria. First, we investigate the cause of the unexpectedly low discriminativeness and show that RL has a deeply rooted side effect of limiting the output words to high-frequency words. The limited vocabulary is a severe bottleneck for discriminativeness as it is difficult for a model to describe the details beyond its vocabulary. Then, based on this identification of the bottleneck, we drastically recast discriminative image captioning as a much simpler task of encouraging low-frequency word generation. Hinted by long-tail classification and debiasing methods, we propose methods that easily switch off-the-shelf RL models to discriminativeness-aware models with only a single-epoch fine-tuning on the part of the parameters. Extensive experiments demonstrate that our methods significantly enhance the discriminative-ness of off-the-shelf RL models and even outperform previous discriminativeness-aware methods with much smaller computational costs. Detailed analysis and human evaluation also verify that our methods boost the discriminativeness without sacrificing the overall quality of captions. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Abusive Supervision and Individual, Organizational Citizenship Behaviour: Exploring the Mediating Effect of Employee Well-Being in the Hospitality Sector

This study intends to bridge the unattended research gap and add to the knowledge base of ‘human resource management’ regarding the relationships between abusive supervision, and individual organizational citizenship behaviour (OCBI), through the mediation of ‘employee well-being’. For the given purpose, a sample of 250 cases was selected to collect data from non-managerial hotel employees from the metropolitan cities of Pakistan. Given responses were analysed in Smart PLS 3.0. Structural equation modelling (PLS-SEM) was used to conduct the necessary tests regarding measurement model and structural model assessment. The study found statistical support for three of the four hypotheses, confirming the deleterious role of abusive supervision in general and the intervening role of employee well-being. The findings have concluded that abusive supervision is harmful for workplaces, particularly when it comes to employees’ citizenship behaviours. Finally, the predictive relevance and r-squared values for the underlying model were also confirmed.
Review on deep learning based techniques for person re-identification

In-depth study has recently been concentrated on human re-identification, which is a crucial component of automated video surveillance. Re-identification is the act of identifying someone in photos or videos acquired from other cameras after they have already been recognized in an image or video from one camera. Re-identification, which involves generating consistent labelling between several cameras, or even just one camera, is required to reconnect missing or interrupted tracks. In addition to surveillance, it may be used in forensics, multimedia, and robotics.Re-identification of the person is a difficult problem since their look fluctuates across many cameras with visual ambiguity and spatiotemporal uncertainty. These issues can be largely caused by inadequate video feeds or lowresolution photos that are full of unnecessary facts and prevent re-identification. The geographical or temporal restrictions of the challenge are difficult to capture. The computer vision research community has given the problem a lot of attention because of how widely used and valuable it is. In this article, we look at the issue of human re-identification and discuss some viable approaches.
Implementation of Hand Gesture-Controlled Mouse Using Artificial Intelligence

This article presents a proposed mouse system. In this paper, we discussed the implementation of an artificially intelligent hand gesture-controlled mouse that uses computer vision to execute mouse functions using the Colour detection technique. The virtual mouse uses the current python and computer vision algorithms for the recognition of the Masked/colored region and works seamlessly without any extra hardware requirements. A computer may be controlled remotely using hand motions, and it is capable to perform cursor movement, left-clicking, and right-clicking without the need for a hardware mouse.
Deep learning based missing object detection and person identification: an application for smart CCTV

Security and protection are the most crucial concerns in today’s quickly developing world. Deep Learning methods and computer vision assist in resolving both problems. One of the computer vision subtasks that allows us to recognise things is object detection. Videos are a source that is taken into account for detection, and image processing technology helps to increase the effectiveness of state-ofthe-art techniques. With all of these technologies, CCTV is recognised as a key element. Using a deep convolutional neural network, we accept CCTV data in real time in this article. The main objective is to make content the centre of things. Using the YOLO technique, we were able to detect the missing item with an improvement of 10% sparsity over the current state-of-the-art algorithm in the context of surveillance systems, where object detection is a crucial step. It can be utilised to take immediate additional action.
Semantic rivalry between French deverbal neologisms in -age, -ion and -ment

This study investigates the semantic aspects of the rivalry between French nominalizing suffixes -age, -ion and -ment.To control for lexicalization effects on derivational semantics, a sample of neologisms ending with the three suffixes is examined.A detailed systematic description of base verbs and derived nouns is provided, taking into account their lexical ambiguity.A total of 501 verbnoun pairs are described with respect to lexical aspect, semantic role assignment properties, and nominal semantic type.Different statistical methods are used to evaluate the relative influence of these properties on suffix selection, the predictability of suffix distribution, and the gradient nature of the rivalry between the three suffixes.Results reveal the importance of discriminative properties such as the referential type of the noun and the ability for event-denoting verbs and nouns to have a result state interpretation.Different degrees of distinctiveness and rivalry can be identified between the three suffixes.It appears that -age and -ment compete more with each other than they do with -ion.
Quantum computing 40 years later

Forty years ago, Richard Feynman proposed harnessing quantum physics to build a more powerful kind of computer. Realizing Feynman's vision is one of the grand challenges facing 21st century science and technology. In this article, we'll recall Feynman's contribution that launched the quest for a quantum computer, and assess where the field stands 40 years later.