Reviewerly/exHarmony huggingface dataset:
Do Servant Leadership Self-Efficacy and Benevolence Values Predict Employee Performance within the Banking Industry in the Post-COVID-19 Era: Using a Serial Mediation Approach

Globally, the COVID-19 pandemic has brought significant implications to the workplace and highly impacted employee performance in every organization. In contemporary research, the scholars agree that leadership is one of the critical antecedents to predict employee performance in organizations. However, research is needed to investigate the mediating role of integral factors such as benevolence values (BV) and self-efficacy (SE) in predicting employee performance in the workplace. This study aimed to investigate the impact of key antecedents on employee performance in the banking industry. The findings reveal that the key antecedents, e.g., servant leadership (SL), self-efficacy (SE), and benevolence values (BV), have a direct positive relationship with employee performance (EP). Moreover, multiple indirect paths were tested, including serial mediation. This study used a quantitative methodology based on the positivist paradigm. A sample of 560 employees was randomly chosen. A survey questionnaire was distributed among them, and 400 were returned with a response rate of 70%, and the clean data of 400 employees was used for data analysis. The structural equation modeling (SEM) technique was employed using Smart PLS 3.3.3 software. The results confirmed that both SE and BV mediate the relationship between SL and EP. Likewise, BV mediates the relationship between SE and EP, and SE mediates the relationship between SL and BV. Finally, in serial mediation, the relationship between SL and EP is also established via SE and BV together as mediators.
Distributed thinking through making: Towards a relational ontology in practice-led design research

Practice-led design research is a celebrated but debated field of inquiry.Although it offers appropriate tools to advance design knowledge through and within making, its scope remains limited to the scale of individual practice.Such a limitation hinders the possibility to account for particular design instances in relation to more general contexts.To address this issue, the paper at hand presents an exploratory literature review discussing why practice-led design research may benefit from adopting a relational ontology-i.e., a stance wherein to be is to relate.The review identifies two streams of relational thinking that exhibit potential overlaps with practice-led design research: sociomateriality and distributed cognition theory.In revealing these overlaps, I introduce the term "distributed thinking through making" to formulate a novel framework from which to reconsider the ontological dimension of practice in practice-led design research.The term illuminates a research gap that appears especially relevant to empirical studies in which making constitutes both the platform and the focus of inquiry.
Fixed-Target Runtime Analysis

Runtime analysis aims at contributing to our understanding of evolutionary algorithms through mathematical analyses of their runtimes. In the context of discrete optimization problems, runtime analysis classically studies the time needed to find an optimal solution. However, both from a practical and from a theoretical viewpoint, more fine-grained performance measures are needed to gain a more detailed understanding of the main working principles and their resulting performance implications. Two complementary approaches have been suggested: fixed-budget analyses and fixed-target analyses. In this work, we conduct an in-depth study on the advantages and the limitations of fixed-target analyses. We show that, different from fixed-budget analyses, many classical methods from the runtime analysis of discrete evolutionary algorithms yield fixed-target results without greater effort. We use this to conduct a number of new fixed-target analyses. However, we also point out examples where an extension of existing runtime results to fixed-target results is highly non-trivial.
How Blockchain Make Better the Supply Chain in the Automotive Industry

The traditional SC is a model that meets the supply expectations of intermediaries. There are gaps in this model, such as relationships between members of the supply chain or consumers' ignorance of information about the source of the products. This article presents a new SC model with Blockchain. To do this, we carried out a comparison between the traditional supply chain and the new chain with the Blockchain in order to designate a model that meets our needs. This BKC method is a new technology that offers effective and confident solutions in businesses. This new model reinforces the reliability of the system based on transparency and traceability in all chain operations, assets and financial means and removes many of the disadvantages of the traditional supply chain. In addition, the Blockchain coordinates all transactions and multi-agents in the SC
Effects of intrinsic decoherence on discord-like correlation measures of two-qubit spin squeezing model

Quantum decoherence happens when the system interacts with the environment. Quantum correlation behaviours in the two-qubit spin squeezing model are studied under the influence of intrinsic decoherence. Quantitative results were determined, which depend on parameters of the physical system by checking different quantifiers of quantum correlation such as entanglement, local quantum uncertainty, trace distance discord and uncertainty-induced quantum nonlocality. We show that the entanglement suffers from intrinsic decoherence and exhibits sudden death, whereas the other measures are more robust against intrinsic decoherence. Further, we highlight the role of spin squeezing coupling constant and magnetic field.
Teachers 'and students' ideas about the existing advantages and possible risks of using digital products in the educational environment

An integral feature and an urgent task of the modern education system is the use of digital products in educational practice. The forced implementation of mass distance learning in the period 2020-21 showed both the advantages and disadvantages of digital learning and set new horizons for optimizing this process. In May-June 2021, a study consisting of two parts was conducted: 1) an online survey of teachers of the general education system in order to determine the ideas about the advantages and risks for students when using digital products in educational practice; 2) an online survey of adolescent students in order to determine their ideas and experience of using digital products in the learning process. The sample consisted of 482 adolescents aged 13-15 years; 147 teachers of basic education with work experience from 3 to 23 years. Online surveys were conducted anonymously in the form of questionnaires using Google Forms. It was found that 80% of teachers and 98% of students highly appreciate the need to use digital products in educational practice. The majority of teachers noted the predominance of advantages provided by digital products as opposed to risks, the same idea was revealed in the survey of students. Conclusions are drawn about the similarities and differences in the understanding of teachers and students of the goals, forms and advantages of using digital educational products. For example, teachers believe that digital products should be used to copy educational materials and perform independent work tasks; they do not welcome their use during the lesson, which, on the contrary, is the main goal for students. The revealed discrepancies in the views of teachers and students regarding the goals, advantages and risks of using digital products makes the question of finding ways to coordinate and optimize relevant.
IE-IQA: Intelligibility Enriched Generalizable No-Reference Image Quality Assessment

Image quality assessment (IQA) for authentic distortions in the wild is challenging. Though current IQA metrics have achieved decent performance for synthetic distortions, they still cannot be satisfactorily applied to realistic distortions because of the generalization problem. Improving generalization ability is an urgent task to make IQA algorithms serviceable in real-world applications, while relevant research is still rare. Fundamentally, image quality is determined by both distortion degree and intelligibility. However, current IQA metrics mostly focus on the distortion aspect and do not fully investigate the intelligibility, which is crucial for achieving robust quality estimation. Motivated by this, this paper presents a new framework for building highly generalizable image quality model by integrating the intelligibility. We first analyze the relation between intelligibility and image quality. Then we propose a bilateral network to integrate the above two aspects of image quality. During the fusion process, feature selection strategy is further devised to avoid negative transfer. The framework not only catches the conventional distortion features but also integrates intelligibility features properly, based on which a highly generalizable no-reference image quality model is achieved. Extensive experiments are conducted based on five intelligibility tasks, and the results demonstrate that the proposed approach outperforms the state-of-the-art metrics, and the intelligibility task consistently improves metric performance and generalization ability.
HandVoxNet++: 3D Hand Shape and Pose Estimation Using Voxel-Based Neural Networks

3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. Existing methods addressing it directly regress hand meshes via 2D convolutional neural networks, which leads to artifacts due to perspective distortions in the images. To address the limitations of the existing methods, we develop HandVoxNet++, i.e., a voxel-based deep network with 3D and graph convolutions trained in a fully supervised manner. The input to our network is a 3D voxelized-depth-map-based on the truncated signed distance function (TSDF). HandVoxNet++ relies on two hand shape representations. The first one is the 3D voxelized grid of hand shape, which does not preserve the mesh topology and which is the most accurate representation. The second representation is the hand surface that preserves the mesh topology. We combine the advantages of both representations by aligning the hand surface to the voxelized hand shape either with a new neural <i>Graph-Convolutions-based Mesh Registration</i> (GCN-MeshReg) or classical segment-wise <i>Non-Rigid Gravitational Approach</i> (NRGA++) which does not rely on training data. In extensive evaluations on three public benchmarks, i.e., SynHand5M, depth-based HANDS19 challenge and HO-3D, the proposed HandVoxNet++ achieves the state-of-the-art performance. In this journal extension of our previous approach presented at CVPR 2020, we gain <inline-formula><tex-math notation="LaTeX">$41.09\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$13.7\%$</tex-math></inline-formula> higher shape alignment accuracy on SynHand5M and HANDS19 datasets, respectively. Our method is <i>ranked first</i> on the HANDS19 challenge dataset ( <i>Task 1: Depth-Based 3D Hand Pose Estimation</i> ) at the moment of the submission of our results to the portal in August 2020.
Rol de la familia en la educación virtual del nivel inicial

The pandemic severely widened the education gap worldwide and particularly in Ecuador. As is known, the educational institutions of the different levels were affected, they suspended the face-to-face modality and implemented virtuality. This situation led the families to assume the role of support and control to the students in a direct way. Hence, this work analyzes the integration strategies used institutionally to promote the participation of families in virtual education at the initial level. The information was collected through a broad theoretical study, considering that it is a qualitative research of descriptive level and; It was analyzed using the Atlas ti 9.0 software. The data reveal that parents have difficulties to adequately support the education of their children or represented. It is also found that a high number of schoolchildren remain alone in their homes during the day or accompanied by older adults and even siblings, due to the fact that adults are involved in work activities. What has been described, leads to conclude that the deficient support of parents to schoolchildren in virtual education processes significantly affects their learning and consequently their comprehensive training.
(Re-)Opening an encounter in the virtual world of Second Life

This study explores how ‘gatherings’ turn into ‘encounters’ in a virtu­al world (VW) context. Most communication technologies enable only focused encounters between distributed participants, but in VWs both gatherings and encounters can occur. We present close sequential analysis of moments when after a silent gathering, inter­action among participants in a VW is gradually resumed, and also investigate the social actions in the verbal (re-)opening turns. Our findings show that like in face-to-face situations, also in VWs partici­pants often use different types of embodied resources to achieve the transition, rather than rely on verbal means only. However, the tran­sition process in VWs has distinctive characteristics compared to the one in face-to-face situations. We discuss how participants in a VW use virtually embodied pre-beginnings to display what we call encounter-readiness, instead of displaying lack of presence by avatar stillness. The data comprise 40 episodes of video-recorded team in­teractions in a VW.
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference in Image Recognition

This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based UI in DNN-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer. Unlike existing UI methods for DNNs, which only calculate the means or modes of the DNN outputs' distributions, the proposed MoGMM-FC layer acts as a probabilistic interpreter for the features that are inputs of the classifier to directly calculate the probabilities of them for the DS-UI. In addition, we propose a dual-supervised stochastic gradient-based variational Bayes (DS-SGVB) algorithm for the MoGMM-FC layer optimization. Unlike conventional SGVB and optimization algorithms in other UI methods, the DS-SGVB not only models the samples in the specific class for each Gaussian mixture model (GMM) in the MoGMM, but also considers the negative samples from other classes for the GMM to reduce the intra-class distances and enlarge the inter-class margins simultaneously for enhancing the learning ability of the MoGMM-FC layer in the DS-UI. Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection. We further evaluate the DS-UI in open-set out-of-domain/-distribution detection and find statistically significant improvements. Visualizations of the feature spaces demonstrate the superiority of the DS-UI. Codes are available at https://github.com/PRIS-CV/DS-UI.
Attacking Video Recognition Models with Bullet-Screen Comments

Recent research has demonstrated that Deep Neural Networks (DNNs) are vulnerable to adversarial patches which introduce perceptible but localized changes to the input. Nevertheless, existing approaches have focused on generating adversarial patches on images, their counterparts in videos have been less explored. Compared with images, attacking videos is much more challenging as it needs to consider not only spatial cues but also temporal cues. To close this gap, we introduce a novel adversarial attack in this paper, the bullet-screen comment (BSC) attack, which attacks video recognition models with BSCs. Specifically, adversarial BSCs are generated with a Reinforcement Learning (RL) framework, where the environment is set as the target model and the agent plays the role of selecting the position and transparency of each BSC. By continuously querying the target models and receiving feedback, the agent gradually adjusts its selection strategies in order to achieve a high fooling rate with non-overlapping BSCs. As BSCs can be regarded as a kind of meaningful patch, adding it to a clean video will not affect people’s understanding of the video content, nor will arouse people’s suspicion. We conduct extensive experiments to verify the effectiveness of the proposed method. On both UCF-101 and HMDB-51 datasets, our BSC attack method can achieve about 90% fooling rate when attacking three mainstream video recognition models, while only occluding &lt; 8% areas in the video. Our code is available at https://github.com/kay-ck/BSC-attack.
The Relationship Between Preschool Teachers’ Proactive Personality and Innovative Behavior: The Chain-Mediated Role of Error Management Climate and Self-Efficacy

Objective: This study, aims to explore the relationship of error management climate and self-efficacy between preschool teachers' proactive personality and innovative behavior. Methods: Four hundred thirty-nine preschool teachers were tested by proactive personality scale, error management climate scale, general self-efficacy scale, and employee innovation behavior scale. Results: Preschool teachers' proactive personality can directly predict their innovative behaviors, has a significant indirect effect on innovative behaviors through error management climate, and has a significant indirect effect on innovative behaviors through self-efficacy. Error management climate and self-efficacy play a chain-mediated role in the relationship between preschool teachers' proactive personality and innovative behavior. Conclusion: Error management climate and self-efficacy play a chain-mediated role in the relationship between preschool teachers' proactive personality and innovative behavior.
Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

The application of artificial intelligence techniques in computer aided detection and diagnosis problems has been among the most promising areas with interest from the scientific community and healthcare industry.Recently, deep learning has become the prime tool for such application with many studies focusing on developing variants that optimize diagnostic performance.Despite the widely accepted success of this class of techniques in this application by the scientific community, it is not prudent to consider it as the only tool available for such purpose.In particular, statistical machine learning offers a variety of techniques that can also be applied at a much lower computational cost.Unfortunately, the results from both strategies cannot be directly compared due to the differences in experimental setups and datasets used in available research studies.Therefore, we focus in this study on this direct comparison using the same dataset and similar data preprocessing as the input to both.We compare statistical machine learning to deep learning in the context of computeraided detection of breast cancer from mammographic images.The results are compared using diagnostic performance metrics and suggest that simpler statistical machine learning techniques may provide better performance with simpler architectures that allow explanation of results.
Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification

In a criminal investigation, along with processing forensic evidence, different investigative techniques are used to identify the perpetrator of the crime. It includes collecting and analyzing unconstrained face images, mostly with low resolution and various qualities, making identification difficult. Since police organizations have limited resources, in this paper, we propose a novel method that utilizes off-the-shelf solutions (Dlib library Histogram of Oriented Gradients-HOG face detectors and the ResNet faces feature vector extractor) to provide practical assistance in unconstrained face identification. Our experiment aimed to establish which one (if any) of the basic image enhancement techniques should be applied to increase the effectiveness. Results obtained from three publicly available databases and one created for this research (simulating police investigators’ database) showed that resizing the image (especially with a resolution lower than 150 pixels) should always precede enhancement to improve face detection accuracy. The best results in determining whether they are the same or different persons in images were obtained by applying sharpening with a high-pass filter, whereas normalization gives the highest classification scores when a single weight value is applied to data from all four databases.
All-microwave nonadiabatic multiqubit geometric phase gate for superconducting qubits

Geometric phase gates are promising tools toward robust quantum computing owing to their robustness against certain control errors and decoherence. Here, we propose a multiqubit architecture with a nonadiabatic geometric phase gate scheme that is feasible in widely used superconducting qubit designs such as transmon and fluxonium. Through segmented microwave drive on multiple qubits coupled off-resonantly to a common resonator, the geometric phase gate is obtained from the qubit-state-dependent displacement of the resonator without fine-tuning the qubit frequencies. Fidelity above 99.99% is achieved in simulation under the available experimental parameters. Our scheme uses all-microwave control and only exploits the lowest qubit levels with long coherence time; thus it is desirable for experiments. Together with the single-qubit holonomic gates demonstrated in earlier experiments, our scheme can realize universal all-geometric quantum computing, and it also finds applications in quantum simulation with many-body interactions.
Twitter Sentiment Analysis Classification in the Arabic Language using Long Short-Term Memory Neural Networks

The increasing use of social media and the idea of extracting meaningful expressions from renewable and usable data which is one of the basic principles of data mining has increased the popularity of Sentiment Analysis which is an important working area recently and has expanded its usage areas. Compiled messages shared from social media can be meaningfully labeled with sentiment analysis technique. Sentiment analysis objectively indicates whether the expression in a text is positive, neutral, or negative. Detecting Arabic tweets will help for politicians in estimating universal incident-based popular reports and people’s comments. In this paper, classification was conducted on sentiments twitted in the Arabic language. The fact that Arabic has twisted language features enabled it to have a morphologically rich structure. In this paper we have used the Long Short Term Memory (LSTM), a widely used type of the Recurrent Neural Networks (RNNs), to analyze Arabic twitter user comments. Compared to conventional pattern recognition techniques, LSTM has more effective results in terms of having less parameter calculation, shorter working time and higher accuracy.